Stochastic Multi-Objective Optimization

Stochastic Multi-Objective Optimization — Multi-criteria optimization under uncertainty with probabilistic objectives or constraints

Multi-objective optimization under uncertainty

Various (Fonseca, Fleming, Deb, Zitzler, and others)

Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.

Numerical objectives and constraints with uncertain/random parameters

Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester.

Caramia, M., Dell'Olmo, P. (2008). Multi-Objective Management in Freight Logistics. Springer, London.

multi-objective-optimization nsga-ii stochastic-dynamic-programming monte-carlo-simulation robust-multi-objective-optimization stochastic-genetic-algorithm


Stochastic NSGA-II

Stochastic Non-dominated Sorting Genetic Algorithm II

S-NSGA-II
NSGA-II under Uncertainty
Stochastic Multi-Objective NSGA-II
Robust NSGA-II

Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions

Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.

Evolutionary multi-objective optimization under uncertainty

Objective function values with stochastic noise or probabilistic constraints

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

Hughes, E. J. (2001). Evolutionary multi-objective ranking with uncertainty and noise. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), Lecture Notes in Computer Science, vol. 1993, pp. 329–343. Springer.

nsga-ii stochastic-multi-objective-optimization multi-objective-genetic-algorithm stochastic-genetic-algorithm stochastic-particle-swarm-optimization robust-nsga-ii


Stochastic Optimization

Stochastic Optimization (SGD and Variants)

Stokastik Optimizasyon (SGD & Varyantları)

Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.

Herbert Robbins & Sutton Monro (SGD, 1951); Diederik Kingma & Jimmy Ba (Adam, 2015)

SGD, SGD with momentum, AdaGrad, RMSProp, Adam, AdamW

Output: Converged parameter vector minimizing the objective function

Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.

Kingma, D.P. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR 2015).

gradient-descent bayesian-optimization evolutionary-strategy robust-optimization neural-networks


Stochastic Particle Swarm Optimization

Stochastic PSO
SPSO
Randomized PSO
Probabilistic PSO

Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community

Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design.

Metaheuristic optimization — stochastic swarm intelligence

Continuous or mixed numerical decision variables; fitness function evaluations

Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.

Clerc, M., Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.


Stochastic Queueing Simulation

Stochastic Queueing Simulation — Probabilistic Modeling of Waiting-Line Systems

SQS
Probabilistic Queueing Simulation
Stochastic Queue Modeling
Random Queueing Simulation

Stochastic Queueing Simulation models waiting-line systems where arrival and service processes follow probability distributions rather than fixed rates. By simulating thousands of random events, it estimates performance measures — mean waiting time, queue length, server utilization — under realistic uncertainty, making it the standard tool for designing and evaluating service systems from hospitals to call centers.

Stochastic simulation — waiting-line system analysis

Inter-arrival times, service times, queue discipline parameters (stochastic distributions)

Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.

Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education.

queueing-simulation discrete-event-simulation markov-model monte-carlo-simulation stochastic-discrete-event-simulation stochastic-markov-model


Stochastic Scenario Analysis

Stochastic Scenario Analysis — Probabilistic multi-scenario evaluation under uncertainty

Probabilistic Scenario Analysis
SSA
Stochastic What-If Analysis
Monte Carlo Scenario Analysis

Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition

Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.

Probabilistic scenario enumeration and evaluation

Uncertain parameters with probability distributions; discrete or continuous scenario sets

Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer.

Lempert, R. J., Popper, S. W., Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation.

scenario-analysis monte-carlo-simulation stochastic-dynamic-programming sensitivity-analysis stochastic-linear-programming risk-analysis


Stochastic Sensitivity Analysis

Stochastic Sensitivity Analysis (Probabilistic Sensitivity Analysis)

PSA
Probabilistic Sensitivity Analysis
Stochastic SA
Monte Carlo Sensitivity Analysis

Saltelli, A. et al.; Claxton, K. et al. (health economics stream)

Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty.

Probabilistic uncertainty quantification technique

Numerical model inputs with probability distributions

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.

Briggs, A. H., Claxton, K., Sculpher, M. (2012). Decision Modelling for Health Economic Evaluation. Oxford University Press.

monte-carlo-simulation scenario-analysis stochastic-scenario-analysis sensitivity-analysis stochastic-markov-model stochastic-discrete-event-simulation


Stochastic System Dynamics

SSD
stochastic stock-flow modelling
probabilistic system dynamics
random system dynamics

Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers

Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios.

Time-series data, probability distributions, expert elicitation

Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill.

Rahmandad, H., Sterman, J.D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998-1014.

system-dynamics monte-carlo-simulation stochastic-differential-equations discrete-event-simulation agent-based-modelling sensitivity-analysis


Stochastic Tabu Search

Stochastic Tabu Search — Randomized metaheuristic optimization with tabu memory

STS
Randomized Tabu Search
Probabilistic Tabu Search
Noisy Tabu Search

Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s)

Stochastic Tabu Search (STS) is an extension of classical Tabu Search that introduces randomness into the neighborhood exploration and move-selection phases. By combining tabu memory — which forbids recently visited solutions — with probabilistic acceptance or random candidate sampling, STS escapes local optima more effectively and explores rugged solution landscapes that deterministic TS may fail to traverse.

Combinatorial or continuous objective function evaluations

Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94.

Hu, J., Fu, M. C., & Marcus, S. I. (2007). A model reference adaptive search method for global optimization. Operations Research, 55(3), 549-568.

tabu-search simulated-annealing stochastic-simulated-annealing genetic-algorithm stochastic-genetic-algorithm particle-swarm-optimization


STOCHASTIC-UTA

STOCHASTIC-UTA (Stochastic UTilités Additives (preference-disaggregation under uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) — STOCHASTIC-UTA seminal chapter Jacquet-Lagrèze, E.; Siskos, J. (1982) — classical UTA foundation Siskos, Y. (1980) — preference disaggregation theory in 1982 — stochastic extension Stavrou-Ventikos-Tsoukalas 2018 Springer. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Preference disaggregation with LP utility fitting + Monte Carlo acceptability analysis

Stavrou, D. I., Ventikos, N. P., Tsoukalas, V. D. (2018). Robust Evaluation of Risks in Ship-to-Ship Transfer Operations: Application of the STOCHASTIC UTA Multicriteria Decision Support Method. In Lee, P. T. W. & Yang, Z. (Eds.), Multi-criteria Decision Making in Maritime Studies and Logistics (pp. 161–185). Springer.


Stochastic Volatility Model

Stochastic Volatility Model (Heston Model)

Stokastik Volatilite Modeli (Heston, SV)

The stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option price; it is the continuous-time counterpart of GARCH.

Continuous-time stochastic volatility model

Calibration to option prices or historical returns

Square-root (CIR) mean-reverting variance

Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6(2), 327-343.

Gatheral, J. (2006). The Volatility Surface: A Practitioner's Guide. Wiley.

garch-model long-memory-models credit-risk-models high-frequency-microstructure portfolio-optimization-mean-variance


STOP-BANG

STOP-BANG Obstructive Sleep Apnea Screening Questionnaire

pre-operative screening; OSA risk stratification

The STOP-BANG is an 8-item screening tool for identifying patients at risk of obstructive sleep apnea (OSA) before surgery or medical procedures. Developed by Chung and colleagues in 2008, it is widely used in perioperative medicine, primary care, and sleep clinics to quickly stratify OSA risk in both adult patients. The tool demonstrates strong sensitivity for moderate to severe OSA, making it valuable in settings where formal sleep testing is impractical.

Chung, F., Yegneswaran, B., Liao, P., et al. (2008). STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology, 108(5), 812-821.


Stop-Signal Reaction Time

The Stop-Signal Reaction Time (SSRT) task is a behavioral measure of response inhibition and executive control. Participants make rapid responses to go signals but must cancel responses when an occasional stop signal appears. By analyzing how successfully they inhibit responses and estimating the latency of inhibition (Stop-Signal Reaction Time), researchers measure the speed and efficiency of the neural inhibitory processes that enable self-control, impulse control, and behavioral flexibility.

Logan, G. D., Cowan, W. B., & Davis, K. A. (1984). On the ability to inhibit simple and choice reaction time responses: A model and a method. Journal of Experimental Psychology: Human Perception and Performance, 10(2), 276-291.

Verbruggen, F., & Logan, G. D. (2008). Response inhibition in the stop-signal paradigm. Trends in Cognitive Sciences, 12(12), 418-424.

Chambers, C. D., Garavan, H., & Bellgrove, M. A. (2009). Insights into the neural basis of response inhibition. Neuroscience & Biobehavioral Reviews, 33(5), 631-646.

drift-diffusion-model go-no-go-task response-inhibition executive-function


Stope Layout

Stope Layout Optimization for Underground Mining

Stope Design
Underground Mine Layout
Panel Design

Stope layout optimization is the process of designing the size, shape, and spatial arrangement of underground mine excavations (stopes) to maximize ore recovery while maintaining safety and economic viability. It balances the desire for large extraction volumes against rock mechanics constraints and support costs. The layout determines mining productivity, capital investment in support systems, and long-term mine life.

Optimization framework for underground mine excavation design

Brady, B. H. G., & Brown, E. T. (2004). Rock mechanics for underground mining. Springer Science+Business Media.

Langford, J. C., & Diederichs, M. S. (2011). Assessing and managing underground excavation hazards. Norwegian University of Science and Technology.


Stormwater Management

Design and Operation of Urban Stormwater Systems

urban stormwater
runoff management
wet weather control
low-impact development

Stormwater management is the planning and engineering of urban water systems to control, treat, and utilize rainwater runoff from developed areas. Traditional approaches (pipes, detention basins) conveyed runoff rapidly to streams or treatment plants; modern green infrastructure approaches (permeable pavements, bioswales, retention ponds) reduce runoff volume through infiltration and reuse while improving water quality. Stormwater management integrates hydrologic modeling, water quality assessment, and infrastructure design to meet regulatory requirements and climate resilience goals.

Urbonas, B., & Stahre, P. (1993). Stormwater Best Management Practices and Detention. Prentice Hall.

US Environmental Protection Agency. (2012). Stormwater Best Management Practices. EPA 832-R-12-001.

Deletic, A., Delleur, J. W., & Gupta, R. (Eds.). (2012). Integrated Urban Water Management. CRC Press.


Strategic Choice Approach

Strategic Choice Approach (Planning Under Pressure)

SCA
Planning Under Pressure
Stratejik Seçim Yaklaşımı
Interactive Strategic Planning

The Strategic Choice Approach (SCA) is an interactive, workshop-based problem structuring method developed by John Friend and Allen Hickling, first published in 1987 and refined in the definitive third edition of Planning Under Pressure (2005). SCA helps groups of planners and stakeholders manage interconnected decisions under uncertainty by explicitly mapping decision areas, option combinations, and sources of uncertainty before committing to action.

Iterative group-based problem structuring and decision process

Output: Commitment package with deferred and immediate decisions

Friend, J., & Hickling, A. (2005). Planning Under Pressure: The Strategic Choice Approach (3rd ed.). Elsevier.


Strategic Orientation Scale

Strategic Orientation and Strategic Posture Assessment Scale

Strategic Posture Scale
Miller-Friesen Framework

Miles and Snow; extended by Miller and Friesen

Strategic Orientation refers to the fundamental approach an organization adopts when competing in its market, encompassing its competitive strategy, market focus, and organizational design. Miles and Snow's (1978) foundational framework identifies four strategic postures: Defenders (focus on stable market segments, operational efficiency, and incremental innovation), Prospectors (pursue new market opportunities, drive innovation, accept higher risk), Analyzers (balance efficiency and innovation, serve established markets while exploring adjacent opportunities), and Reactors (lack clear strategy, respond reactively to environmental pressures). This scale operationalizes Miles and Snow's framework, revealing an organization's strategic type and fit with its environment and structure.

Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. McGraw-Hill.

Miller, D., & Friesen, P. H. (1983). Strategy-making and environment: The third link. Strategic Management Journal, 4(3), 221–235.

Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. Free Press.

entrepreneurial-orientation-scale dynamic-capabilities-scale market-sensing-capability-scale innovation-ambidexterity-scale absorptive-capacity-scale


Stratified Best Worst Method

Stratified Best Worst Method (Stratified BWM)

Stratified BWM is an extension of the Best Worst Method that applies the BWM logic recursively across multiple hierarchical layers. Instead of weighting criteria at a single level, it identifies the best and worst criterion within each level of a hierarchy, then aggregates weights across levels. This enables more realistic modeling of complex decision problems with natural hierarchical structures.

Hierarchical pairwise comparison with layer-wise best-worst

Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.

Asadzadeh, A., Rezaei, J., & Tavasszy, L. (2017). Sustainability assessment of supplier selection criteria using the BWM method in hierarchical procurement. Supply Chain Management, 22(6), 551-563.

bwm bwm-sort ahp-bocr hierarchical-bwm fuzzy-bwm


Stratified Sampling

Proportional Stratified Sampling
Optimal Allocation Sampling
Stratum-Based Sampling
Tabakalı Örnekleme

Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.

Probability-based survey sampling design

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.


Stratigraphic Correlation

lithostratigraphic correlation
chronostratigraphic correlation
sequence correlation

Stratigraphic correlation is the practice of identifying equivalent rock layers or chronostratigraphic units across space by tracing physical or chemical signatures. Rooted in 19th-century work on Alpine glacial sequences, this method was formalized in the 20th century by geologists like Vail who unified global sea-level change with depositional sequences. Correlation is foundational to basin-scale understanding of sediment transport, resource distribution, and paleoenvironmental change.

Catuneanu, O. (2002). Sequence Stratigraphy of Clastic Systems. Geological Association of Canada.

Vail, P. R., Mitchum, R. M., & Thompson, S. (1977). Global cycles of relative changes of sea level. American Association of Petroleum Geologists Memoir, 26, 83–97.

Posamentier, H. W., & Allen, G. P. (2006). Siliciclastic Sequence Stratigraphy: Concepts and Applications. Society for Sedimentary Geology.

well-log-analysis seismic-reflection-interpretation geologic-mapping petrographic-analysis basin-subsidence-analysis


Straussian Grounded Theory

Straussian Grounded Theory (Strauss & Corbin)

Strauss-Corbin GT
systematic grounded theory
GTM (Straussian)
conditional/consequential matrix GT

1990 (systematic elaboration; building on Glaser & Strauss 1967)

Straussian Grounded Theory is a systematic qualitative methodology developed by Anselm Strauss and Juliet Corbin that generates theory inductively from data through structured coding procedures. Unlike exploratory description, it aims to produce a substantive mid-range theory that explains how a social process unfolds, grounding every theoretical claim directly in empirical evidence collected from participants who have experienced the phenomenon under study.

Interviews, observations, documents, field notes (text data)

20–50 participants or purposive/theoretical sampling until saturation

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

Corbin, J., & Strauss, A. (2008). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (3rd ed.). Sage.

grounded-theory phenomenology ethnography case-study thematic-analysis action-research


SDQ SDQ-Strengths SDQ-Parent SDQ-Teacher

The Strengths and Difficulties Questionnaire (SDQ), developed by Robert Goodman in 1997 and validated by 2001, is a brief 25-item behavioral screening instrument for children aged 2–17 years. Available in parent, teacher, and youth self-report versions, it assesses both emotional/behavioral difficulties and personal strengths, making it unique among screening tools. It is widely used in community, clinical, and research settings for rapid screening and population surveillance.

Multi-informant behavioral screening scale

Goodman, R. (2001). Psychometric properties of the Strengths and Difficulties Questionnaire. Journal of the American Academy of Child & Adolescent Psychiatry, 40(11), 1337-1345.

Stone, L. L., Otten, R., Engels, R. C., et al. (2010). Psychometric properties of the parent and teacher versions of the Strengths and Difficulties Questionnaire for 4- to 12-year-olds. Clinical Child & Family Psychology Review, 13(3), 254-274.

cbcl-child-behavior conners-rating-scales vanderbilt-adhd-scale achenbach-youth-self-report


STRIDE/DREAD Threat Modeling

STRIDE and DREAD Threat Modeling Methodology

threat modeling
security analysis
risk assessment

STRIDE/DREAD Threat Modeling is a Microsoft-developed methodology for systematically identifying and prioritizing security threats in software systems. STRIDE enumerates threat categories (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege), and DREAD scores threats by Damage, Reproducibility, Exploitability, Affected Users, and Discoverability.

Threat identification and risk assessment

Shostack, A. (2008). Threat Modeling: Designing for Security. Microsoft Press.

Howard, M., & Lipner, S. (2006). The Security Development Lifecycle. Microsoft Press.

Schoenfield, B. (2015). Securing the Internet of Things. Apress.


STROBE Checklist

Strengthening the Reporting of Observational Studies in Epidemiology Checklist

STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) is a 22-item evidence-based checklist published in 2007 by Von Elm et al. to improve the quality of reporting of cohort, case-control, and cross-sectional observational studies. Like CONSORT for RCTs, STROBE is endorsed by over 300 journals and is widely considered the reporting standard for observational epidemiological research.

Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. The Lancet, 370(9596), 1453–1457.

newcastle-ottawa-scale consort-reporting-checklist prisma-checklist casp-rct-checklist


SS-QoL

The SS-QoL is a disease-specific quality-of-life instrument designed to capture the multidimensional impact of stroke on survivors' functional and emotional well-being. Developed by Williams and colleagues in 1999, this 49-item scale addresses stroke-specific concerns including language, cognition, mobility, and emotional functioning. It is a gold-standard instrument for stroke outcome research and routine clinical monitoring of post-stroke recovery.

Williams, L. S., Weinberger, M., Harris, L. E., Clark, D. O., & Biller, J. (1999). Development of a Stroke-Specific Quality of Life Scale. Stroke, 30(7), 1362-1369.


Strong Gravitational Lensing

Strong Gravitational Lensing for Mass and Distance Measurements

Strong Lensing
Gravitational Lens
Einstein Ring

Strong gravitational lensing occurs when massive objects (clusters, galaxies) bend light so strongly that multiple images of distant sources appear, or complete rings (Einstein rings) form. Proposed by Sjur Refsdal in 1964 and first observed in 0957+561 in 1979, strong lensing provides direct measurements of lens masses and cosmic distances independent of the distance ladder.

Refsdal, S. (1964). On the possibility of determining Hubble's parameter and the masses of galaxies from the gravitational lens effect. Monthly Notices of the Royal Astronomical Society, 128(4), 307-311.

Walsh, D., Carswell, R. F., & Weymann, R. J. (1979). 0957+ 561 A, B: Twin quasistellar objects or gravitational lens? Nature, 279, 381-384.

Suyu, S. H., et al. (2017). Cosmology from Gravitational Lens Statistics. Space Science Reviews, 212(1), 1-46.


Strontium Provenance

Sr isotope provenance
strontium isotope analysis

Strontium isotope provenance analysis uses the ratios of strontium-87 to strontium-86 in human skeletal remains to determine geographic origin and track human mobility and migration. Developed by Jonathan Ericson in the 1980s, this method exploits the fact that strontium isotope ratios in the environment vary geographically based on underlying geology. When individuals consume food and water from a specific region, they incorporate that region's characteristic strontium isotope signature into their bones and teeth, creating a geochemical fingerprint of their residence.

Ericson, J. E. (1985). Strontium isotope characterization in the study of prehistoric migrations. Journal of Human Evolution, 14(5), 503-514.

Price, T. D., Grupe, G., & Schroter, P. (1994). Reconstruction of migration patterns in the Bell Beaker period by stable lead isotope analysis. Journal of Archaeological Science, 21(6), 697-708.

Bentley, R. A. (2006). Strontium isotopes from the earth to the archaeological skeleton: a review. Journal of Archaeological Method and Theory, 13(3), 135-187.


Stroop Task

The Stroop task is a classic measure of cognitive control and selective attention. Participants name the color of words while ignoring the words' semantic content. When the color and word meaning match (e.g., the word 'red' printed in red ink), responses are fast. When they conflict (e.g., the word 'red' printed in blue ink), response times increase dramatically. This Stroop effect reveals how automatic word reading interferes with color naming, indexed by the difference in reaction times between congruent and incongruent conditions.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643-662.

MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163-203.

Raz, A. (2007). Suggestibility and neuroplasticity of the anterior cingulate cortex. Behavioral and Brain Sciences, 30(1), 96-97.

executive-function cognitive-control attention response-inhibition


Structural Break ADF Unit Root Test

Structural Break Augmented Dickey-Fuller Unit Root Test

ADF with structural break
Perron unit root test
break-augmented ADF
unit root test with structural change

The structural break ADF unit root test extends the standard Augmented Dickey-Fuller test to allow for one or more discrete shifts in the level or trend of a time series. Because ignoring a structural break inflates the apparent persistence of a series, this test prevents false acceptance of the unit root null when the series is actually stationary around a shifting mean or trend.

Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361-1401.

Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.


Structural Break AR Model

Autoregressive Model with Structural Breaks

AR model with structural change
breakpoint AR model
piecewise autoregressive model
AR model with regime shifts

The structural break AR model extends the standard autoregressive framework by allowing the intercept and autoregressive coefficients to shift at one or more unknown break dates. Each regime between consecutive break points is governed by its own AR parameters, capturing abrupt changes in the dynamics of a time series caused by crises, policy shifts, or other shocks.

Time-series model with structural change

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22.

autoregressive-model zivot-andrews-structural-break-test structural-break-arima-model structural-break-var-model augmented-dickey-fuller-unit-root-test structural-break-vecm


Structural Break ARCH Model

Autoregressive Conditional Heteroscedasticity Model with Structural Breaks

ARCH with structural breaks
break-adjusted ARCH
regime-switching ARCH
SB-ARCH

Engle (1982) for ARCH; Lamoureux & Lastrapes (1990) for break-adjusted variance persistence

The Structural Break ARCH model extends Engle's (1982) Autoregressive Conditional Heteroscedasticity framework by explicitly accounting for abrupt, permanent shifts in the conditional variance process. Ignoring structural breaks in variance causes ARCH parameters to appear spuriously persistent, so incorporating break dummies or regime-specific parameters yields more accurate volatility estimates and better model fit.

Financial or macroeconomic time series with heteroscedastic errors

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.

Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business and Economic Statistics, 8(2), 225–234.

arch-model garch-model structural-break-garch-model zivot-andrews-structural-break-test egarch-model tgarch-model


Structural Break ARDL Bounds Test

Structural Break Autoregressive Distributed Lag Bounds Test

SB-ARDL bounds test
ARDL bounds test with structural break
Fourier ARDL bounds test
break-augmented bounds testing

Pesaran, Shin & Smith (bounds framework); structural break extensions by Bahmani-Oskooee, Enders & Jones, and others

The structural break ARDL bounds test extends the Pesaran, Shin and Smith (2001) bounds testing framework to accommodate one or more structural breaks in the long-run relationship between time-series variables. By incorporating break dummies or smooth Fourier terms into the ARDL error-correction equation, it allows researchers to test for cointegration even when the data have experienced shifts in intercept or slope caused by policy changes, crises, or regime switches.

Time-series (levels and first differences, with break dummies or Fourier terms)

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.

Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.

ardl-bounds-test nonlinear-ardl fourier-ardl-bounds-test zivot-andrews-structural-break-test structural-break-vecm engle-granger-cointegration-test


Structural Break ARIMA Model

Structural Break Autoregressive Integrated Moving Average Model

ARIMA with structural breaks
break-adjusted ARIMA
piecewise ARIMA
ARIMA with regime shifts

Perron (1989); extended by Bai & Perron (1998)

A structural break ARIMA model extends the standard ARIMA framework by explicitly identifying and accommodating one or more abrupt shifts in the level, trend, or dynamics of a time series. Rather than forcing a single set of ARIMA parameters across the entire sample, it fits separate ARIMA specifications for each regime defined by the detected break dates.

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.

arima-model bai-perron-test chow-test threshold-autoregressive-model markov-switching-model unit-root-test


Structural break DCC-GARCH

Structural Break Dynamic Conditional Correlation GARCH Model

DCC-GARCH with structural breaks
break-adjusted DCC-GARCH
regime-shift DCC-GARCH
SB-DCC-GARCH

Engle (2002) for DCC; break-augmented extensions by Pelletier (2006) and subsequent literature

Structural break DCC-GARCH extends Engle's Dynamic Conditional Correlation GARCH framework by explicitly allowing the correlation and volatility structure to shift at one or more structural break points in the sample. It models time-varying co-volatility between multiple financial series while accounting for sudden regime changes caused by crises, policy shifts, or market microstructure changes.

Multivariate volatility model with regime change

Multivariate financial time series (returns); continuous

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.

Pelletier, D. (2006). Regime switching for dynamic correlations. Journal of Econometrics, 131(1-2), 445-473.

dcc-garch-model structural-break-garch-model structural-break-egarch structural-break-tgarch zivot-andrews-structural-break-test vector-autoregression


Structural Break Difference GMM

Structural Break Difference Generalized Method of Moments

Difference GMM with structural breaks
break-augmented Arellano-Bond GMM
dynamic panel GMM with regime shifts
structural change Difference GMM

Arellano & Bond (Difference GMM); Bai & Perron (structural break testing)

Structural Break Difference GMM extends the Arellano-Bond first-difference GMM estimator to dynamic panel settings where the data-generating process shifts at one or more unknown breakpoints. By explicitly incorporating break indicators or allowing regime-specific parameters, the estimator avoids the biased coefficient and invalid moment conditions that arise when a structural change is ignored in a standard Difference GMM fit.

Dynamic panel estimator with structural breaks

Balanced or unbalanced panel data; continuous and discrete outcomes

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.

difference-gmm system-gmm arellano-bond-gmm-estimator structural-break-system-gmm structural-break-panel-data-analysis dynamic-panel-data-model


Structural Break Dynamic Panel Data Model

Dynamic Panel Data Model with Structural Breaks

panel dynamic structural break estimator

Bai & Perron (break detection); Arellano & Bond (dynamic panel GMM)

The structural break dynamic panel data model extends the standard dynamic panel framework by allowing regression coefficients or the autoregressive parameter to shift at one or more unknown break dates. It combines GMM-based dynamic panel estimation with formal structural change tests, enabling researchers to study how economic relationships evolve across distinct regimes while controlling for unobserved individual heterogeneity and endogeneity of the lagged dependent variable.

Balanced or unbalanced panel (N cross-sections, T time periods)

dynamic-panel-data-model arellano-bond-gmm-estimator panel-system-gmm zivot-andrews-structural-break-test structural-break-panel-data-analysis panel-vecm


Structural Break EGARCH

Exponential GARCH Model with Structural Breaks

SB-EGARCH
EGARCH with regime shifts
break-adjusted EGARCH
structural change EGARCH

Nelson (1991) for EGARCH; Lamoureux and Lastrapes (1990) for break-augmented GARCH variants

Structural Break EGARCH combines Nelson's Exponential GARCH framework with explicit allowance for one or more structural breaks in the volatility process. By letting the intercept and persistence parameters of the log-variance equation shift at detected break dates, the model avoids the spurious long-memory and inflated persistence that standard EGARCH suffers when the data contain regime changes.

Financial or macroeconomic time series, continuous

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.

egarch-model arch-model tgarch-model dcc-garch-model zivot-andrews-structural-break-test structural-break-garch-model


Structural break Engle-Granger cointegration

Structural Break Engle-Granger Cointegration Test

Gregory-Hansen cointegration test
cointegration with structural break
EG cointegration with regime shift
residual-based cointegration with break

Gregory & Hansen (1996), extending Engle & Granger (1987)

The structural break Engle-Granger cointegration test, most commonly implemented via the Gregory-Hansen (1996) procedure, extends the classical Engle-Granger two-step test to allow for a single unknown structural break in the long-run cointegrating relationship. It tests whether two or more integrated series share a common stochastic trend even when that relationship may have shifted at some point in the sample.

Cointegration test with structural break

Integrated (I(1)) time series with possible regime shifts

Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99-126.

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276.

engle-granger-cointegration johansen-cointegration zivot-andrews-unit-root ardl-bounds-test error-correction-model hatemi-j-cointegration


Structural Break Fixed Effects Model

Fixed Effects Model with Structural Breaks

structural change fixed effects estimator

1998 (Bai-Perron); FE estimator classical

Bai & Perron (structural break testing); Mundlak / within-group estimator tradition

The structural break fixed effects model extends the standard within-group (FE) panel estimator by allowing the slope coefficients to shift at one or more detected break dates. Each unit's unobserved time-invariant heterogeneity is still removed by demeaning, but separate coefficient regimes are estimated for each sub-period, capturing policy shifts, crises, or technological transitions that would otherwise bias a single-regime FE estimate.

Balanced or unbalanced panel data (multiple units, multiple time periods)

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.

fixed-effects-model panel-fixed-effects-model zivot-andrews-structural-break-test structural-break-random-effects-model panel-hausman-test structural-break-panel-data-analysis


Structural Break GLS

Generalized Least Squares with Structural Breaks

GLS with structural breaks
break-adjusted GLS
structural change GLS
regime-switching GLS

1998 (structural break GLS formalization)

Bai & Perron (1998); GLS framework by Aitken (1936)

Structural Break GLS combines Generalized Least Squares estimation with explicit allowance for regime shifts in the data-generating process. The method estimates separate coefficient vectors for each segment defined by detected break dates while correcting for non-spherical errors — heteroscedasticity or autocorrelation — that frequently accompany structural change, yielding consistent and efficient estimates across all regimes.

Time series or cross-section with heteroscedastic or autocorrelated errors and regime changes

Greene, W. H. (2012). Econometric Analysis (7th ed.). Prentice Hall.

structural-break-ols structural-break-wls generalized-least-squares zivot-andrews-structural-break-test panel-gls robust-gls


Structural Break Granger Causality

Granger Causality Testing with Structural Breaks

break-robust Granger causality
Granger causality under regime change
time-varying Granger causality
structural change Granger test

Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)

Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time.

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.

Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398-1410.

granger-causality vector-autoregression toda-yamamoto-causality zivot-andrews-unit-root rolling-window-regression threshold-var


Structural Break Hausman Test

Hausman Specification Test with Structural Break Correction

structural change Hausman specification test

1978 (base); extended through 1990s–2000s

Jerry A. Hausman (base test, 1978); structural break extension developed in panel econometrics literature

The Structural Break Hausman Test extends the classical Hausman (1978) specification test to panel or time-series settings where the data-generating process shifts at one or more break points. By detecting structural breaks first and then running the Hausman comparison within each regime, researchers can reliably choose between fixed effects and random effects estimators even when the underlying relationship changes over time.

Panel data or time series with suspected regime changes

Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.

Perron, P. (2006). Dealing with structural breaks. In T. C. Mills & K. Patterson (Eds.), Palgrave Handbook of Econometrics, Vol. 1 (pp. 278–352). Palgrave Macmillan.

panel-hausman-test fixed-effects-model random-effects-model zivot-andrews-structural-break-test structural-break-fixed-effects-model structural-break-random-effects-model


Structural break Johansen cointegration

Johansen Cointegration Test with Structural Breaks

Johansen cointegration with breaks
break-robust Johansen test
cointegration test with regime shifts
structural change Johansen VECM

Johansen (1988); structural-break extensions by Saikkonen & Lütkepohl (2000) and Lütkepohl, Müller & Saikkonen (2001)

The structural break Johansen cointegration test extends the standard maximum-likelihood Johansen procedure to settings where the multivariate time series exhibits level shifts or trend breaks. By incorporating dummy variables or shift regressors into the VECM, the test determines the cointegrating rank without confounding genuine long-run relationships with regime changes.

Multivariate time series; I(1) or near-I(1) variables

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.

Saikkonen, P., & Lütkepohl, H. (2000). Testing for the cointegrating rank of a VAR process with structural shifts. Journal of Business and Economic Statistics, 18(4), 451–464.


Structural Break KPSS Test

KPSS Stationarity Test with Structural Breaks

KPSS test with breaks
structural break stationarity test
KPSS break test
SB-KPSS

Kurozumi (2002); Carrion-i-Silvestre, Del Barrio & Lopez-Bazo (2005)

The structural break KPSS test extends the standard Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test to allow for one or more known or unknown structural breaks in the level or trend of a time series. Under the null hypothesis the series is stationary around a broken deterministic component, enabling researchers to distinguish genuine unit-root behaviour from apparent non-stationarity caused by regime shifts.

Stationarity test with structural breaks

Carrion-i-Silvestre, J. L., Del Barrio, T., & Lopez-Bazo, E. (2005). Breaking the panels: An application to the GDP per capita. Econometrics Journal, 8(2), 159-175.

Kurozumi, E. (2002). Testing for stationarity with a break. Journal of Econometrics, 108(1), 63-99.


Structural Break MA Model

Moving Average Model with Structural Breaks

MA model with structural change
broken MA model
MA with regime shift
structural break moving average

A Moving Average (MA) time series model augmented to accommodate one or more structural breaks — abrupt shifts in the mean, variance, or MA coefficients occurring at known or unknown break dates. Ignoring structural breaks in an MA process inflates forecast errors and distorts inference on the error dynamics.

Time series model with structural change

Univariate time series with suspected regime shifts

Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401.

Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.

structural-break-arima-model structural-break-ar-model moving-average-model zivot-andrews-structural-break-test structural-break-arma-model arima-model


Structural Break NARDL

Structural Break Nonlinear Autoregressive Distributed Lag Model

SB-NARDL
NARDL with structural breaks
nonlinear ARDL with break
asymmetric ARDL structural break

Shin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchers

Structural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a richer picture of asymmetric and break-sensitive dynamics between variables.

Nonlinear cointegration with structural breaks

Time series (univariate or multivariate), continuous

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.

nonlinear-ardl arima-model zivot-andrews-structural-break-test engle-granger-cointegration-test vector-error-correction-model structural-break-ardl-bounds-test


Structural Break OLS

Ordinary Least Squares Regression with Structural Breaks

OLS with structural breaks
piecewise OLS
regime-switching OLS
breakpoint regression

Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation

Structural Break OLS extends ordinary least squares to allow regression coefficients to shift at one or more breakpoints in time or across regimes. Rather than forcing a single coefficient vector across the entire sample, the model partitions the data and estimates a separate OLS regression within each segment, making it appropriate when economic relationships are suspected to change due to policy shifts, crises, or other structural events.

Time series or cross-section with regime changes

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.

ols-regression zivot-andrews-structural-break-test augmented-dickey-fuller-unit-root-test arima-model vector-autoregression structural-break-gls


Structural Break Panel Data Analysis

Structural Break Analysis in Panel Data Models

panel structural break test
break-point panel model
panel change-point analysis
regime-shift panel analysis

Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al.

Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identification of regime shifts than single-series break tests, and it delivers separate coefficient estimates for each regime before and after each break.

Panel time-series model with regime shifts

Balanced or unbalanced panel (cross-sectional units observed over time)

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.

Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.

panel-fixed-effects panel-cointegration panel-unit-root-test difference-in-differences threshold-regression time-series-structural-break


Structural break PP unit root test

Structural Break Phillips-Perron Unit Root Test

Phillips-Perron test with structural break

Pierre Perron (building on Phillips & Perron)

The structural break Phillips-Perron (PP) unit root test extends the classical PP framework to allow for one or more discrete shifts in the level or trend of a time series. By endogenously or exogenously identifying break dates and controlling for them, it tests the null of a unit root against a trend-stationary alternative that accommodates structural change, avoiding the spurious acceptance of non-stationarity caused by ignored breaks.

Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80(2), 355-385.

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

adf-unit-root-test zivot-andrews-unit-root-test pp-unit-root-test kpss-stationarity-test lee-strazicich-unit-root-test lumsdaine-papell-unit-root-test


quantile-on-quantile with structural breaks

Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology

Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full distributional space but also across distinct historical periods or policy regimes.

Nonparametric quantile regression with structural breaks

Sim, N., and Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.

Bai, J., and Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.

quantile-on-quantile-regression quantile-regression structural-break-ardl-bounds-test structural-break-granger-causality zivot-andrews-structural-break-test nonlinear-ardl


Structural Break Random Effects Model

Random Effects Panel Model with Structural Breaks

RE model with structural breaks
break-adjusted random effects
random effects break model
panel RE with regime shifts

Bai & Perron (break detection); Baltagi (panel RE framework)

The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution.

Balanced or unbalanced panel data (cross-sectional units over time)

Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). Wiley.

structural-break-fixed-effects-model random-effects-model panel-random-effects-model zivot-andrews-structural-break-test structural-break-panel-data-analysis panel-hausman-test


Structural Break SARIMA Model

Structural Break Seasonal Autoregressive Integrated Moving Average Model

SARIMA with structural breaks
break-augmented SARIMA
piecewise SARIMA
SARIMA-SB

Box & Jenkins (SARIMA); Bai & Perron (structural break detection)

The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at estimated breakpoints and fits separate SARIMA processes to each resulting segment, producing more accurate forecasts and reliable inference in the presence of regime changes.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

sarima-model arima-model structural-break-test bai-perron-test seasonal-decomposition threshold-autoregressive-model


Structural break SVAR model

Structural Vector Autoregression with Structural Breaks

break-SVAR
SVAR with regime change
structural break structural VAR
SB-SVAR

Sims (1980) for SVAR; structural break extensions developed throughout 1990s–2000s

The structural break SVAR model extends the standard Structural Vector Autoregression by allowing one or more discrete shifts in the system's parameters across time. It simultaneously identifies causal (structural) shocks and accounts for regime changes — such as policy shifts, crises, or institutional reforms — that alter the dynamics among multiple time series.

Multivariate time-series model with regime change

Multivariate macroeconomic or financial time series

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.

structural-var structural-break-var-model vector-error-correction-model structural-break-vecm zivot-andrews-structural-break-test structural-break-ardl-bounds-test


Structural Break System GMM

Structural Break System Generalized Method of Moments

System GMM with structural breaks
SB-SGMM
break-augmented System GMM
System GMM structural change estimator

Blundell & Bond (System GMM); Bai & Perron (structural break framework)

Structural Break System GMM extends the Blundell-Bond System GMM estimator for dynamic panel data by explicitly accounting for structural breaks — abrupt regime changes in slopes, intercepts, or dynamics — that, if ignored, bias the coefficient estimates and invalidate the moment conditions that underpin standard GMM inference.

Dynamic panel estimator with regime change

Panel data (cross-section × time series) with potential structural breaks

Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22.

system-gmm difference-gmm arellano-bond-gmm-estimator structural-break-difference-gmm dynamic-panel-data-model panel-system-gmm


Structural Break TGARCH

SB-TGARCH
threshold GARCH with structural breaks
GJR-GARCH with structural breaks
break-adjusted TGARCH

Lamoureux & Lastrapes (structural breaks in GARCH); Glosten, Jagannathan & Runkle (TGARCH/GJR-GARCH asymmetry)

Structural Break TGARCH extends the Threshold GARCH (GJR-GARCH) model to accommodate discrete, permanent shifts in the volatility process. By detecting structural breaks and incorporating them — either as regime-specific intercepts or dummy variables — the model separates genuine volatility persistence from spurious persistence induced by ignored regime changes, and preserves the asymmetric leverage effect that characterises equity and financial return data.

High-frequency or daily financial time series

Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225-234.

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.

garch-model egarch-model tgarch-model structural-break-garch markov-switching-garch bai-perron-breakpoint-test


Structural Break Toda-Yamamoto Causality

Toda-Yamamoto Causality Test with Structural Breaks

structural break modified Wald test causality

1995 (base); structural break extensions widely adopted 2000s–2010s

Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature

The structural break Toda-Yamamoto causality test extends the standard Toda-Yamamoto modified Wald (MWALD) procedure to accommodate one or more structural breaks in the time series. By identifying break dates first and then including dummy variables in the augmented VAR, the test maintains its valid asymptotic chi-squared distribution regardless of the integration or cointegration order of the variables, even in the presence of regime shifts.

Time series (univariate or multivariate)

Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.

Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.

toda-yamamoto-causality-test granger-causality-test structural-break-granger-causality zivot-andrews-structural-break-test vector-autoregression structural-break-var-model


Structural Break VAR Model

Vector Autoregression Model with Structural Breaks

VAR with structural breaks
break-point VAR
regime-switching VAR
SB-VAR

Bai & Perron (structural breaks); Sims (VAR framework)

The Structural Break VAR model extends the standard Vector Autoregression (VAR) framework by allowing coefficient matrices and error covariance to shift at one or more unknown break dates. It is designed for multivariate time series where economic relationships change abruptly due to policy shifts, financial crises, or major structural events.

Multivariate time series model with regime change

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.

vector-autoregression structural-var structural-break-vecm zivot-andrews-structural-break-test vector-error-correction-model structural-break-arima-model


Structural break VECM

Vector Error Correction Model with Structural Breaks

cointegration model with structural breaks

Gregory & Hansen (1996); Johansen, Mosconi & Nielsen (2000)

The Structural Break VECM extends the standard Vector Error Correction Model to allow the cointegrating relationships, adjustment speeds, or short-run dynamics to shift at one or more known or estimated break dates. It preserves the long-run equilibrium framework of the VECM while explicitly modelling regime changes caused by policy shifts, crises, or institutional changes.

Multivariate error correction model with structural breaks

Multivariate integrated time series (I(1)) with cointegrating relations

Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126.

Johansen, S., Mosconi, R., & Nielsen, B. (2000). Cointegration analysis in the presence of structural breaks in the deterministic trend. Econometrics Journal, 3(2), 216–249.

vector-error-correction-model johansen-cointegration-test structural-break-var-model zivot-andrews-structural-break-test structural-break-johansen-cointegration nonlinear-vecm


Structural Break WLS

Weighted Least Squares with Structural Break Correction

WLS with structural change
break-corrected WLS
segmented WLS
structural break weighted regression

1998 (break framework); WLS long-established

Bai & Perron (structural break framework); WLS classical

Structural Break WLS combines Weighted Least Squares estimation with explicit detection and correction for structural breaks — abrupt regime shifts — in the data. By identifying break points and assigning observation-level weights that account for heteroscedasticity within and across regimes, the estimator delivers consistent, efficient coefficient estimates even when the error variance changes dramatically at a break.

Cross-sectional or time-series data with heteroscedasticity and structural breaks

Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.

Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.

structural-break-ols structural-break-gls panel-wls zivot-andrews-structural-break-test robust-wls weighted-least-squares


Structural break Zivot-Andrews test

Structural Break Zivot-Andrews Unit Root Test

The Zivot-Andrews test is an endogenous structural break unit root test that determines the break point from the data rather than imposing it externally. It tests for a unit root against the alternative of stationarity around a single structural break — in the mean, the trend, or both — choosing the break date that provides the strongest evidence against the null.

Unit root test with endogenous structural break

Univariate time series (levels or first differences)

Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.

Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401.


Structural Equation Modeling

SEM
path analysis
latent variable modeling
causal modeling

Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.

Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.


Structural Form-Finding

Structural Form-Finding and Optimal Structural Design

form-finding algorithm
structural optimization
funicular design

Structural Form-Finding is a computational method for discovering structural geometries that are efficient under given loads and constraints. Pioneered by Heinz Schek in 1974, it reverses traditional structural design: rather than imposing a predetermined form and then analyzing whether it is strong enough, form-finding begins with loads and support conditions and derives the optimal form that minimizes material use while meeting safety requirements.

computational structural optimization method

Schek, H. J. (1974). The Force Density Method for Form Finding and Computation of General Networks. Computer Methods in Applied Mechanics and Engineering, 3(1), 115-134.

Kilian, A., Ochsendorf, J. (2009). Particle-Spring Systems for Structural Form Finding. Journal of the International Association for Shell and Spatial Structures, 46(2), 77-84.

Hensel, M., Menges, A., Weinstock, M. (2006). Techniques and Technologies in Morphogenetic Architecture. Architectural Design, 76(2), 88-95.


Structural Health Monitoring

SHM
damage detection monitoring
condition monitoring of structures
vibration-based structural monitoring

Non-destructive evaluation and condition monitoring

1980s–1990s (formalized as a discipline ~1993–2001)

Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)

Structural Health Monitoring (SHM) is a process-based engineering methodology used in civil, mechanical, and aerospace engineering to continuously assess the condition of structures — bridges, buildings, dams, pipelines, and aircraft — through embedded or attached sensor networks. By acquiring real-time or periodic measurement data and applying signal processing and statistical pattern recognition, SHM aims to detect, locate, classify, and quantify damage before it reaches a critical state, enabling evidence-based maintenance decisions.

Engineering monitoring and diagnostic framework

Sensor time-series (acceleration, strain, displacement, acoustic emission)

Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 303–315.

Farrar, C. R., & Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective. Wiley.

finite-element-analysis modal-analysis non-destructive-testing fatigue-analysis reliability-analysis digital-twin


Structural Time Series Model

Basic Structural Model (Structural Time Series Model)

BSM
basic structural model
unobserved components model
Yapısal Zaman Serisi Modeli (BSM)

The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.

State-space (unobserved components) time series model

Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.

Harvey, A. C. & Shephard, N. (1993). Structural Time Series Models. In G. S. Maddala, C. R. Rao & H. D. Vinod (Eds.), Handbook of Statistics, Vol. 11 (pp. 261-302). Elsevier.

arima exponential-smoothing bayesian-structural-time-series markov-switching var-model


Structural VAR

SVAR
structural vector autoregression
identified VAR
structural VAR model

Sims (1980); identification schemes by Blanchard & Quah (1989)

Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.

Multivariate time series (stationary or cointegrated)

Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48.

vector-autoregression vector-error-correction-model granger-causality-test arma-model johansen-cointegration-test dynamic-panel-data-model


Structured Clinical Interview for DSM

The Structured Clinical Interview for DSM Disorders (SCID) is a semi-structured interview protocol designed to assess the presence or absence of DSM diagnostic criteria for major psychiatric disorders. Developed by Michael B. First and colleagues in the 1990s and updated to align with DSM-5, it remains the gold-standard diagnostic instrument in clinical research and clinical practice.

First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured Clinical Interview for DSM-5 Disorders—Clinician Version (SCID-5-CV). American Psychiatric Association.

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured Clinical Interview for DSM-IV Axis I Disorders. Biometrics Research Department, New York State Psychiatric Institute.


Structured Interview

standardized interview
formal interview
schedule-based interview
fixed-format interview

Survey research tradition; formalized by Campbell, Katona, and Kahn in mid-20th century

A structured interview is a data collection technique in which every participant is asked exactly the same pre-specified questions in the same order, using standardized wording. Because the interview schedule is fixed, responses across participants are directly comparable, enabling quantitative aggregation and statistical analysis. It sits at the most standardized end of the interview continuum, between the self-administered questionnaire and the semi-structured interview.

Quantitative / mixed data collection technique

Verbal responses to fixed questions (converted to categorical or numeric data)

Fontana, A., & Frey, J. H. (2000). The interview: From structured questions to negotiated text. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (2nd ed., pp. 645–672). Sage.

Campbell, A., & Katona, G. (1953). The sample survey: A technique for social science research. In L. Festinger & D. Katz (Eds.), Research Methods in the Behavioral Sciences (pp. 15–55). Dryden Press.

survey semi-structured-interview in-depth-interview focus-group questionnaire telephone-assisted-structured-interview


SPJ Framework

Structured Professional Judgment Framework for Risk Assessment

SPJ
Structured Professional Judgment
SPJ Framework

Stephen D. Hart, Peter R. Kropp, David R. Laws

The Structured Professional Judgment (SPJ) framework represents a contemporary approach to forensic risk assessment that synthesizes clinical judgment with empirical evidence of risk factors. Rather than producing a numerical score, SPJ guides clinicians through systematic evaluation of case-specific evidence to arrive at a structured, transparent categorical risk judgment. SPJ has become the preferred methodology in many forensic settings globally and underlies instruments such as the HCR-20v3 (violence risk) and sexual offender assessment protocols.

Hart, S. D., Kropp, P. R., & Laws, D. R. (Eds.). (2003). Sexual deviance: Theory, assessment, and treatment. Guilford Press.

Kropp, P. R., Hart, S. D., & Belfrage, H. (2008). Structured professional judgment as an approach to violence risk assessment. In K. A. Van den Berg, P. T. Mason, & B. K. Waller (Eds.), Risk assessment and management of violent offenders (pp. 85–109). Springer.

hcr-20 violence-risk-appraisal-guide saprof level-of-service-inventory


Structured Text Extraction

Structured Data Extraction (Form & Table Extraction)

Yapılandırılmış Veri Çıkarma (Form & Tablo Çıkarma)

Structured text extraction is a document-processing pipeline that automatically identifies and pulls tables, form fields, and structured data from PDF, HTML, and scanned documents. It converts heterogeneous document layouts into machine-readable, analysis-ready records and is widely used in data collection workflows, document digitisation projects, and academic corpus construction.

Input: PDF, HTML, or scanned document images

Output: Structured tables, form fields, or key-value pairs

OCR quality directly affects extraction accuracy

Zhu, J. et al. (2021). TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content. ACL.

Zhong, X. et al. (2020). Image-Based Table Recognition. ECCV.

named-entity-recognition information-extraction ocr-post-correction document-classification pdf-parsing


SCPS

Student Clinical Placement Satisfaction Scale

Clinical Placement Satisfaction
Placement Experience Scale
Clinical Site Satisfaction

The SCPS is a self-report questionnaire measuring students' overall satisfaction with their clinical placement experience, including satisfaction with the learning environment, educator support, clinical opportunities, and facility resources. Originally developed by Papastavrou and colleagues in Cyprus (2007–2010), the SCPS evaluates multiple aspects of placement quality that contribute to student wellbeing and learning outcomes. The scale is used to monitor placement satisfaction across programs, compare satisfaction across clinical sites, and identify areas for improvement in clinical education delivery.

Papastavrou, E., Lambrinou, E., Tsangari, H., & Varthy, M. (2010). Students' satisfaction with their clinical learning environment in Cyprus: A longitudinal study. Nurs Educ Today 30(6): 539–544.

Levett-Jones, T., Gersbach, J., Arthur, C., & Roche, J. (2007). Implementing a web-based learning environment to enhance clinical education. Nurse Educ Today 27(6): 580–588.

clinical-learning-environment-scale clinical-teaching-quality-scale ripls nursing-clinical-competence-scale


Student Engagement Scale

Jennifer Fredricks, Phyllis Blumenfeld, Alison Paris

The Student Engagement Scale (SES) measures the extent to which students are actively involved in academic and social aspects of school or university life. Grounded in Fredricks et al.'s multidimensional framework, the instrument assesses behavioral engagement (participation, attendance, effort), emotional engagement (interest, belonging, enthusiasm), and cognitive engagement (mental effort, deep thinking, persistence). High engagement is consistently associated with better learning outcomes and well-being.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.

Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758-773.

academic-motivation-scale student-satisfaction-survey sense-of-belonging-scale school-climate-scale


Student Satisfaction Survey

Contemporary educational assessment practices

The Student Satisfaction Survey (SSS) is a widely used institutional tool to measure student perceptions of course quality, instructor effectiveness, and learning environment. Typically administered at the end of a course using Likert-scale items, the SSS collects feedback on teaching methods, course materials, support services, and overall satisfaction, providing institutions with actionable data for continuous improvement.

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.

Elliott, K. M., & Shin, D. (2003). Student satisfaction: An alternative approach to assessing this important construct. Journal of Higher Education Policy and Management, 25(2), 97-109.

course-experience-questionnaire teaching-effectiveness-scale school-climate-scale student-engagement-scale


Study Process Questionnaire

The Study Process Questionnaire (SPQ) is a self-report instrument developed by John Biggs to identify the approaches and processes students use when learning. It assesses three dimensions: deep learning approach (seeking understanding and making connections), surface learning approach (memorizing and reproducing), and achieving/strategic approach (aiming for high grades). Understanding students' study processes helps educators identify learning challenges and tailor support.

Biggs, J. B. (1987). Study Process Questionnaire: A New Instrument for Assessing Ways of Learning. Educational Research and Perspectives, 14(1), 1-8.

Biggs, J. (2001). Teaching for Quality Learning at University. Open University Press.


Study Skills Assessment Questionnaire

Study Skills Assessment Questionnaire (SSAQ)

The Study Skills Assessment Questionnaire measures the habitual study practices, time management, concentration, and learning motivation of students. Originating from the foundational Survey of Study Habits and Attitudes (Brown & Holtzman, 1964) and refined in contemporary versions, the SSAQ identifies whether students employ evidence-based study techniques (spaced practice, self-testing, active recall) or ineffective strategies (cramming, passive rereading). This information is invaluable for academic support professionals designing skill-building interventions tailored to student needs.

Brown, W. F., & Holtzman, W. H. (1964). Survey of Study Habits and Attitudes (SSHA). Psychological Corporation.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70.

academic-burnout-scale procrastination-assessment-scale test-anxiety-inventory academic-resilience-scale academic-help-seeking-scale


Stuttering Severity Instrument

The Stuttering Severity Instrument–Fourth Edition (SSI-4) is the standard clinician-administered measure of stuttering severity in children (ages 2–13) and adults (ages 14–75). Developed by Riley (2009), SSI-4 quantifies stuttering through three behavioral components: frequency (percentage of syllables stuttered), duration (average length of stuttering moments), and physical concomitants (observable tension and associated movements). SSI-4 Severity Scores enable reliable tracking of treatment response, prognosis estimation, and comparison across populations, making it essential for evidence-based stuttering assessment and research.

Riley, G. D. (2009). Stuttering Severity Instrument for Children and Adults–Fourth Edition (SSI-4). Austin, TX: Pro-Ed Publications.

Riley, G. D., & Riley, J. (1994). The Stuttering Severity Instrument for Children and Adults–Third Edition: A Revised Assessment Tool. Seminars in Speech and Language, 15(3), 196–201.

Beilby, J. M., Byrnes, M. L., & Yaruss, J. S. (2012). Acceptance and Commitment Therapy for Adolescents Who Stutter. Journal of Fluency Disorders, 37(4), 290–299.


Threshold and Smooth-Transition VAR

Threshold Vector Autoregression and Smooth-Transition Vector Autoregression (TVAR / STVAR)

Eşik VAR ve Geçiş Değişkenli VAR (TVAR / STVAR)

Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.

Nonlinear multivariate time-series model

Regime-wise least squares with threshold/transition search

Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.

Balcilar, M. et al. (2017). Regime-Dependent Effects of Uncertainty Shocks. Economic Modelling.

markov-switching var-model egarch gjr-garch arch-lm-test


Subgame Perfect Equilibrium

Subgame Perfect Equilibrium (SPE) with Backward Induction

Backward Induction
Sequential Equilibrium
Extensive-Form Equilibrium

Subgame Perfect Equilibrium (SPE) is a refinement of Nash Equilibrium for sequential games, introduced by Reinhard Selten in 1965. It requires that strategy profiles constitute a Nash Equilibrium in every subgame, eliminating non-credible threats and incredible promises. Backward induction is the primary computational method for finding SPE in finite games.

Selten, R. (1965). Spieltheoretische Behandlung eines Oligopolmodells mit Nachfrageträgheit. Zeitschrift für die gesamte Staatswissenschaft, 121, 301-324.

von Stackelberg, H. (1934). Marktform und Gleichgewicht. Julius Springer.

nash-equilibrium bayesian-nash-equilibrium stackelberg-competition evolutionary-game-theory


Subjective Well-Being Scale

The Subjective Well-Being (SWB) Scale is a broad category of brief instruments measuring how satisfied people are with their lives and the frequency of positive and negative emotions they experience. Originating from Diener's foundational work in the 1980s, SWB scales operationalize the recognition that well-being is fundamentally subjective—how people evaluate their lives matters more than external objective conditions. Various forms exist, including the Satisfaction with Life Scale (SWLS), the Subjective Happiness Scale (SHS), and multi-item composites measuring life satisfaction, positive affect, and negative affect.

Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46(2), 137–155.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71–75.

who-5-wellbeing-index flourishing-scale perma-scale positive-mental-health-scale


Subjectivity Detection

Subjectivity Detection (Subjective vs. Objective Classification)

Öznellik Tespiti (Subjectivity Detection)

Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis.

Output: Subjectivity label / score (0 = objective, 1 = subjective)

Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.

Pang, B. & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Proceedings of ACL.


SASSI

The SASSI is a comprehensive self-report inventory designed to identify substance abuse and dependence through both direct and indirect assessment methods. Developed by Glenn Miller in 1997 and updated to the SASSI-3 format, it employs 'subtle' items that indirectly measure substance abuse risk without openly asking about drug or alcohol use, thereby reducing response bias and improving detection in individuals who may be motivated to minimize their substance use. The SASSI is widely used in clinical, occupational health, and criminal justice settings.

Miller, G. A. (1997). The Substance Abuse Subtle Screening Inventory-2 (SASSI-2) manual. Spencer, IN: Spencer Psychology Press.

Lazowski, L. E., Miller, F. G., Boye, M. W., & Miller, G. A. (2010). A comparison of the Substance Abuse Subtle Screening Inventory-3 (SASSI-3) to the SASSI-2 in a large correctional sample. Addictive Behaviors, 35(4), 333–340.

dudit sadq cudit brief-addiction-monitor


Substitution Reaction Kinetics

nucleophilic substitution kinetics
SN kinetics
reaction kinetics

Substitution reaction kinetics analysis is the systematic study of how fast nucleophiles replace leaving groups in organic and inorganic compounds. Formalized by Edward Hughes and Christopher Ingold in the 1930s, this framework distinguishes between bimolecular (SN2) and unimolecular (SN1) mechanisms, connecting mechanism to reaction rates, and enabling prediction of reactivity based on substrate structure, nucleophile strength, and solvent effects.

Hughes, E. D., & Ingold, C. K. (1937). Mechanism of substitution at a saturated carbon atom. Part IV. A discussion of relative reactivities in different solvents. Journal of the Chemical Society, 527–537.

Lowry, T. H., & Richardson, K. S. (2002). Mechanism and Theory in Organic Chemistry (3rd ed.). Longman.


Subsynchronous Resonance

Subsynchronous Resonance Analysis in Power Systems

SSR
Subsynchronous control interactions
Torsional oscillations

Subsynchronous Resonance (SSR) is a phenomenon where frequencies below the synchronous frequency (50/60 Hz) are amplified in power systems, causing oscillations that can damage turbines. First observed in Bushland, Texas in 1977, SSR results from interaction between series-compensated transmission lines and synchronous generators. Understanding and mitigating SSR is critical for stable grid operation, particularly with high levels of series compensation or power electronics.

Identification and mitigation of subsynchronous oscillations in AC systems

Farmer, R. G., Natel, B., & Schulz, R. P. (1977). The bushland event of September 10, 1977. IEEE Transactions on Power Apparatus and Systems, 96(4), 1315-1328.

Hingorani, N. G. (1988). Subsynchronous resonance in power systems. IEEE Power Engineering Review, 8(5), 5-12.

Kimbark, E. W. (1971). Power System Stability. Wiley & Sons.


SPS

SPS
Suicide Probability Scale
Cull-Gill SPS

The Suicide Probability Scale (SPS) is a 36-item self-report instrument developed by John Cull and William Gill (1990) to assess suicide risk, hopelessness, suicide ideation, negative self-evaluation, and hostility in adolescents and adults. It provides a multidimensional profile of suicide-related cognitions and emotions and is used in clinical, psychiatric, school, and forensic settings to screen for suicide risk and guide treatment planning.

Cull, J. G., & Gill, W. S. (1990). Suicide Probability Scale (SPS): Professional manual. Western Psychological Services.

Cull, J. G., & Gill, W. S. (1985). Suicide Probability Scale: A validity study with adolescents and young adults. Psychological Reports, 57(2), 451–459.

beck-hopelessness-scale novaco-anger-scale hcr-20 level-of-service-inventory


Sundial

Sundial (Generative Time-Series Foundation Models)

Time-Series Foundation Model (Generative)

Sundial is a family of generative time-series foundation models introduced by Yong Liu and colleagues at Tsinghua University (ICML 2025). Pre-trained on large and diverse time-series corpora, Sundial employs a decomposition-based architecture paired with a generative forecasting head to produce probabilistic multi-horizon forecasts. It represents a shift toward general-purpose, zero-shot-capable models for real-world temporal prediction tasks.

Generative time-series foundation model family

Large-scale pre-training on diverse time-series corpora

TimeMixer-style decomposition with generative forecasting head

Liu, Y., Qin, G., Shi, X., Hu, T., Wang, J., & Long, M. (2025). Sundial: A family of highly capable time series foundation models. ICML.


Sunyaev-Zel'dovich Effect

Sunyaev-Zel'dovich Effect for Galaxy Cluster Detection

SZ Effect
Inverse Compton Scattering
SZE

The Sunyaev-Zel'dovich effect is a phenomenon in which the cosmic microwave background (CMB) is distorted as photons travel through hot gas in galaxy clusters. Proposed by Rashid Sunyaev and Yakov Zel'dovich in 1972, this effect provides a powerful method for detecting distant galaxy clusters and measuring fundamental cosmological parameters without distance assumptions.

Sunyaev, R. A., & Zel'dovich, Y. B. (1972). The observations of the relic radiation as a test of the nature of X-ray radiation from clusters of galaxies. Comments on Astrophysics and Space Physics, 4(4), 173-178.

Carlstrom, J. E., Holder, G. P., & Reese, E. D. (2002). Cosmology with the Sunyaev-Zel'dovich effect. Annual Review of Astronomy and Astrophysics, 40, 643-680.

Planck Collaboration (2014). Planck 2013 results. XXVII. Doppler boosting of the Sunyaev-Zel'dovich effect. Astronomy & Astrophysics, 571, A27.


Super-Efficiency DEA

Super-Efficiency Data Envelopment Analysis

Andersen-Petersen Model
Super-Radial DEA
Ranking DEA
Süper Etkinlik VZA

Super-Efficiency DEA is a nonparametric linear programming extension of classical Data Envelopment Analysis (DEA) introduced by Andersen and Petersen (1993). While standard DEA assigns a maximum efficiency score of 1.0 to all units on the efficient frontier, Super-Efficiency DEA allows efficient units to receive scores greater than 1.0 by temporarily removing the evaluated unit from the reference set. This modification enables full ranking of all decision-making units (DMUs), including those previously indistinguishable at the frontier.

Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.


Supercritical Fluid Extraction

SFE
supercritical CO2 extraction
supercritical carbon dioxide extraction
dense gas extraction

Separation science / green extraction technology

1960s–1980s (industrial development; Zosel's foundational patents ~1964–1978)

Multiple contributors (Kurt Zosel, Gerd Brunner, and others from the 1960s–1980s)

Supercritical Fluid Extraction (SFE) is a separation technique that uses a fluid held above its critical temperature and pressure — most commonly carbon dioxide — to selectively dissolve and remove target compounds from a solid or liquid matrix. Widely applied in food science, nutraceutical production, and the flavour and fragrance industry, SFE offers a solvent-efficient, thermally gentle route to recovering oils, antioxidants, pigments, and bioactive compounds without the toxic residues associated with conventional organic solvents.

Physical separation and extraction technique

Yield (mass), purity (%), composition (GC-MS or HPLC profiles)

Brunner, G. (2005). Supercritical fluids: technology and application to food processing. Journal of Food Engineering, 67(1–2), 21–33.

Reverchon, E., & Senatore, F. (1994). Supercritical carbon dioxide extraction of chamomile essential oil and its analysis by gas chromatography-mass spectrometry. Journal of Agricultural and Food Chemistry, 42(1), 154–158.

solvent-extraction steam-distillation cold-press-extraction liquid-liquid-extraction membrane-separation chromatography


Supply Chain Integration Scale

Supply Chain Integration Capability Scale

SCI Scale
Supply Chain Collaboration Scale

Supply Chain Integration (SCI) refers to an organization's capacity to seamlessly coordinate and align processes, information, and incentives across internal functions and with external suppliers and customers. Flynn et al. (2010) operationalized SCI into three complementary dimensions in the Journal of Operations Management: internal integration (coordination across departments), supplier integration (collaboration with upstream partners), and customer integration (collaboration with downstream partners). Organizations with high SCI reduce costs through process alignment, improve quality through shared information, and accelerate time-to-market through coordinated innovation. This scale has become foundational in supply chain management research and practice.

Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.

Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management, 19(2), 185–200.

Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R. (2003). The effects of an integrative supply chain strategy on customer service and supply chain performance: An empirical study. Journal of Operations Management, 21(5), 523–539.

market-sensing-capability-scale absorptive-capacity-scale dynamic-capabilities-scale organizational-resilience-scale knowledge-management-scale


Support Team Assessment Schedule

The Support Team Assessment Schedule (STAS) is a clinician-rated observational instrument assessing the impact of palliative care support on patients with advanced illness and their families across seven key domains: pain, symptoms, anxiety, family well-being, communication, and support adequacy. Developed by Baker, Speck, and Cohen in 1997, the STAS has become a standard quality-of-life outcome measure in community palliative care, hospice, and research, enabling teams to systematically monitor the effectiveness of their interventions and identify patients and families in crisis.

Baker, A., Speck, P., & Cohen, D. (1997). Support Team Assessment Schedule (STAS): Development of a new instrument for the evaluation of support to patients and families in palliative care. Journal of Palliative Care, 13(2), 39–45.

Grande, G. E., Todd, C. J., & Barclay, S. I. (2009). Support Team Assessment Schedule (STAS): A framework for assessing the impact of community palliative care. Journal of Advanced Nursing, 34(6), 699–710.

caregiver-qol-cancer needs-assessment-palliative mcgill-quality-of-life palliative-performance-scale comfort-care-checklist


SURE Test

SURE Test for Decisional Conflict Screening

SURE Screening Tool
Decisional Conflict Screener

The SURE Test is a four-item screening questionnaire designed to rapidly identify patients experiencing decisional conflict—uncertainty or difficulty in making healthcare decisions. Developed by Annette O'Connor and colleagues, the SURE Test is an abbreviated, practical version of the longer Decisional Conflict Scale (DCS), created to detect patients who may benefit from decision support or additional counselling. It is widely used in clinical and research settings for quick, valid screening.

O'Connor, A. M. (2010). Using the Decisional Conflict Scale to evaluate a decision aid. In A. Edwards & G. Elwyn (Eds.), Shared Decision Making in Health Care (pp. 424-438). Oxford University Press.

Légaré, F., Côté, L., Labrecque, M., et al. (2012). Decisional conflict in patients and their physicians: a consensus approach. Social Science & Medicine, 65(11), 2280-2290.

decisional-conflict-scale collaboste-scale control-preferences-scale patient-enablement-instrument


Surface Code Quantum Error Correction

surface code
topological error correction

Surface Code is a two-dimensional topological quantum error-correcting code that protects quantum information through geometric redundancy. Introduced by Alexei Kitaev in 2003, surface code is considered the leading candidate for large-scale fault-tolerant quantum computing due to its high error thresholds and feasibility on two-dimensional qubit arrays.

Kitaev, A. Y. (2003). Fault-tolerant quantum computation by anyons. Annals of Physics, 303, 2–30.

Dennis, E., Kitaev, A., Landau, F., Preskill, J. (2002). Topological quantum memory. Journal of Mathematical Physics, 43, 4452–4505.

Google AI Quantum and Collaborators. (2019). Exponential suppression of bit or phase errors with cyclic codes. arXiv preprint arXiv:1909.04316.


Surface Plasmon Resonance

Surface Plasmon Resonance (SPR) is a real-time, label-free technique for detecting and monitoring biomolecular interactions at a sensor surface by measuring changes in the refractive index caused by ligand binding. Developed by Kretschmann in 1971 and applied to biosensing by Liedberg, Nylander, and Lundström in 1983, SPR is now a gold standard for measuring binding kinetics (association and dissociation rates) and equilibrium binding constants in protein interactions, antibody-antigen recognition, and drug discovery.

Kretschmann, E. (1971). Determination of optical constants of metals by excitation of surface plasmons. Zeitschrift für Physik, 241(4), 313-324.

Liedberg, B., Nylander, C., & Lundström, I. (1983). Surface plasmon resonance for gas detection and biosensing. Sensors and Actuators, 4, 299-304.

Homola, J. (2008). Surface plasmon resonance sensors for detection of chemical and biological species. Chemical Reviews, 108(2), 462-493.


Surrogate-Based Optimization

Surrogate-Based Optimization (Metamodel-Assisted Optimization)

Vekil Model Tabanlı Optimizasyon (Surrogate-Based)

Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)

Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses.

Kriging (Gaussian Process), Radial Basis Functions (RBF), Polynomial Response Surface, Neural Network

Latin Hypercube Sampling (LHS) or other space-filling designs

Cross-validation of surrogate fit is mandatory

Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley.

Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and Analysis of Computer Experiments. Statistical Science, 4(4), 409-423.

latin-hypercube-sampling gaussian-process-regression bayesian-optimization design-of-experiments evolutionary-strategy response-surface-methodology