Nitrogen Use Efficiency

Nitrogen Use Efficiency Assessment and Optimization

NUE analysis
Nitrogen recovery efficiency
N balance assessment

Nitrogen Use Efficiency (NUE) assessment and optimization is an analytical pipeline for evaluating how effectively crops convert applied nitrogen fertilizer into grain, biomass, or economic output. Developed by agronomic researchers (Dobermann, Raun) in the 2000s, this method quantifies nitrogen losses and identifies management practices to improve both crop productivity and environmental sustainability.

Dobermann, A., & Cassman, K. G. (2005). Nitrogen use efficiency in cereals: mechanisms and genetic improvements. In Managing soil quality and crop productivity in intensive agriculture (pp. 15-40). CRC Press.

Raun, W. R., Johnson, G. V., Phillips, S. B., & Westerman, R. L. (2002). Effect of long-term nitrogen fertilization on soil organic C and total N in continuous wheat under rainfed conditions. Journal of Plant Nutrition, 25(12), 2797-2809.

soil-fertility-management crop-growth-simulation crop-yield-estimation irrigation-scheduling-etref precision-agriculture-ndvi


NMD

NMD (New Method of Determining objective criterion weights) is a weight objective multi-criteria decision-making (MCDM) method introduced by Bulut, E. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Objective (normalised matrix column-mean based)

Bulut, E. (2017). A comparative analysis of the criteria weights determination methods for selection of ship propulsion system. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment

ahpsort aploco aras aroman artasi cobra cocoso codas


NMF Topic Model

Non-negative Matrix Factorization Topic Model

NMF
Non-negative Matrix Factorization
NMF for Topic Modeling
NNMF Topic Model

Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.

Matrix factorization / unsupervised topic model

Document-term matrix (non-negative, e.g. TF or TF-IDF counts)

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems (NIPS), 13, 556–562.

lda-topic-model topic-modeling sentence-embeddings bert-based-classification convolutional-neural-network


NMR Spin-Echo

The spin-echo is a fundamental nuclear magnetic resonance (NMR) pulse sequence technique introduced by Erwin Hahn in 1950. It uses a 90-degree radiofrequency pulse followed by a 180-degree refocusing pulse to create an echo, effectively reversing the effects of magnetic field inhomogeneities and allowing accurate measurement of spin relaxation properties. This technique is essential in modern NMR spectroscopy for both one-dimensional and multidimensional experiments.

Hahn, E. L. (1950). Spin echoes. Physical Review, 80(4), 580-594.

Carr, H. Y., & Purcell, E. M. (1954). Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 94(3), 630-638.

Meiboom, S., & Gill, D. (1958). Modified spin-echo method for measuring nuclear relaxation times. Review of Scientific Instruments, 29(10), 688-691.

cosy noesy hsqc ft-icr-mass-spectrometry


No-U-Turn Sampler

NUTS
No-U-Turn HMC
adaptive Hamiltonian Monte Carlo
self-tuning HMC

The No-U-Turn Sampler (NUTS) is a self-tuning Markov chain Monte Carlo algorithm introduced by Hoffman and Gelman (2014) that extends Hamiltonian Monte Carlo (HMC) by automatically determining the optimal number of leapfrog steps, eliminating the most sensitive manual tuning parameter. NUTS is the default sampler in Stan and PyMC and has made large-scale, high-dimensional Bayesian inference practically accessible without requiring users to set trajectory lengths by hand.

Draw samples from posterior distributions without manual tuning of trajectory length

Dual averaging (step size) + No-U-Turn criterion (trajectory length)

Posterior samples, R-hat, ESS, energy diagnostics

Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623.

Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

hamiltonian-monte-carlo metropolis-hastings bayesian-regression mcmc variational-inference hierarchical-bayes


NODDI

Neurite Orientation Dispersion and Density Imaging (NODDI) is a biophysical diffusion MRI model that quantifies microstructural properties of white matter: neurite density (axonal density), orientation dispersion (fiber coherence), and isotropic diffusion (free water or cerebrospinal fluid). Introduced by Zhang and colleagues in 2012, NODDI provides biologically interpretable metrics directly linking diffusion MRI signals to tissue microstructure.

Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.

Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. (2019). Imaging brain microstructure with diffusion MRI: practical clinical applications. Nature Reviews Neurology, 15(10), 591–606.


NOESY

Nuclear Overhauser Enhancement Spectroscopy (NOESY) is a 2D NMR technique that detects through-space dipolar coupling between protons, rather than through-bond scalar coupling. Introduced by Macura and Ernst in 1981, NOESY reveals which protons are spatially close in the three-dimensional structure, independent of bonding connectivity. This makes NOESY invaluable for determining molecular conformation, assigning stereochemistry, and elucidating protein folds.

Aue, W. P., Bartholdi, E., & Ernst, R. R. (1976). Two-dimensional spectroscopy. Application to nuclear magnetic resonance. The Journal of Chemical Physics, 64(5), 2229-2246.

Macura, S., & Ernst, R. R. (1981). Elucidation of cross relaxation in liquids by two-dimensional NMR spectroscopy. Molecular Physics, 41(1), 95-117.

Wüthrich, K. (1986). NMR of Proteins and Nucleic Acids. John Wiley & Sons.


Noise Mapping

Environmental Noise Assessment and Spatial Mapping

noise assessment
acoustic mapping
sound level modeling
environmental noise

Noise mapping is an environmental assessment methodology that quantifies and visualizes sound levels spatially across a study area, enabling identification of noise-exposed populations, compliance with regulatory standards, and design of mitigation measures. Standardized by the European Directive 2002/49/EC and ISO 13442, noise mapping combines acoustic measurements, traffic/industrial source modeling, and geographic information systems (GIS) to create contour maps of sound exposure and associated health impacts.

spatial assessment and modeling pipeline

International Organization for Standardization. (2008). ISO 13442:2008 Acoustics - Description, Measurement and Assessment of Environmental Noise in Relation to Human Exposure and Health.

European Parliament. (2002). Directive 2002/49/EC relating to the Assessment and Management of Environmental Noise.

Kephalopoulos, S., Paviotti, M., & Anfosso-Lédée, F. (2012). Common Noise Assessment Methods in Europe (CNOSSOS-EU). JRC Reference Report EUR 25379 EN.


Nominal Group Technique

NGT
structured group process
nominal group process
priority-setting group method

André L. Delbecq and Andrew H. Van de Ven

The Nominal Group Technique (NGT) is a structured group facilitation method designed to generate and prioritise ideas, problems, or solutions while ensuring equal participation from all members. Developed by Delbecq and Van de Ven in 1971, it combines silent individual idea generation with structured group discussion and systematic voting to produce a ranked list of priorities. Unlike unstructured focus groups, NGT prevents dominant voices from suppressing quieter participants, making it especially valuable for needs assessment, program planning, and stakeholder priority-setting in applied research and policy contexts.

Written ideas, structured verbal discussion, numerical ranking/voting data

5–12 participants per group (multiple groups possible)

Delbecq, A. L., & Van de Ven, A. H. (1971). A group process model for problem identification and program planning. Journal of Applied Behavioral Science, 7(4), 466–492.

Delbecq, A. L., Van de Ven, A. H., & Gustafson, D. H. (1975). Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. Scott Foresman.

focus-group delphi-method action-research thematic-analysis content-analysis mixed-methods


Nomological Validity

nomological network validity
construct network validity
nomological web validity

Nomological validity evaluates whether a construct behaves as theory predicts within a broader network of related constructs. It is not a single statistical test but an accumulation of evidence that the measure fits coherently into a web of theoretically grounded relationships — demonstrating that what is measured is what the theory says it should measure.

Correlational / structural data among theoretically related constructs

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.

convergent-validity discriminant-validity construct-validity confirmatory-factor-analysis structural-equation-modeling content-validity


Nomophobia Questionnaire

NMP-Q
Nomophobia
Fear of Being Without Phone

The Nomophobia Questionnaire measures 'nomophobia'—the fear of being without one's mobile phone—a contemporary form of technology-related psychological distress emerging with smartphone ubiquity. Developed by Yildirim and Correia (2015), the 20-item NMP-Q captures anxiety, compulsive checking, communication apprehension, and negative perceptions about being unreachable, reflecting the extent to which individuals depend on smartphones for functioning and sense of security.

Yildirim, C., & Correia, A. P. (2015). Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Computers in Human Behavior, 49, 130–137.

King, A. L., Valença, A. M., & Silva, A. C. (2013). Nomophobia: Diagnosis and interventions. Computers in Human Behavior, 29(1), 26–32.


Non-linear Best Worst Method

Non-linear Best Worst Method (Non-linear BWM)

Extended development of Rezaei's BWM framework

Non-linear BWM is a variant of the Best Worst Method that replaces the linear programming formulation with non-linear optimization. Instead of minimizing the maximum deviation (Chebyshev distance), it minimizes the sum of squared deviations (L2 norm). This provides more flexible weight derivation and better accommodates uncertain or fuzzy preferences.

Non-linear optimization for flexible weight derivation

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

Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2017). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358-371.

bwm fuzzy-bwm grey-bwm interval-bwm nonlinear-programming


Non-negative Matrix Factorization

Non-negative Matrix Factorization (Lee & Seung, 1999)

NMF
NNMF
nonnegative matrix factorization
non-negative matrix approximation
parts-based matrix decomposition

Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.

Matrix decomposition with non-negativity constraints

Dimensionality reduction, feature extraction, topic modeling, source separation

All factor matrices must be element-wise non-negative

Multiplicative update rules guaranteed to be non-increasing in reconstruction error

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.

Cichocki, A., Zdunek, R., Phan, A. H., & Amari, S. (2009). Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley.

principal-component-analysis latent-dirichlet-allocation singular-value-decomposition independent-component-analysis k-means-clustering


Non-participant Observation

detached observation
systematic observation
structured field observation
external observation

Formalized mid-20th century (Gold 1958); practice dates to late 19th-century social surveys

Raymond Gold (role typology); earlier roots in social survey movement and Chicago School sociology

Non-participant observation is a data-collection method in which the researcher observes behavior, interactions, or events in a natural or structured setting without joining or influencing the activity under study. The observer maintains a deliberate distance from participants to minimize their own effect on the phenomena being recorded, producing field notes, behavioral tallies, or recordings that reflect naturally occurring behavior rather than behavior shaped by researcher involvement.

Qualitative / quantitative observational data collection

Field notes, tallies, behavioral counts, audio-visual recordings

Gold, R. L. (1958). Roles in sociological field observations. Social Forces, 36(3), 217–223.

Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage.

participant-observation structured-interview ethnography field-notes survey systematic-review


Nonlinear ADF Unit Root Test

Nonlinear Augmented Dickey-Fuller Unit Root Test

KSS test
nonlinear unit root test
ESTAR unit root test
Kapetanios-Shin-Snell test

The Nonlinear ADF unit root test, most prominently operationalized by Kapetanios, Shin, and Snell (2003), extends the classical Augmented Dickey-Fuller test to detect mean reversion that occurs via an Exponential Smooth Transition Autoregressive (ESTAR) process. It tests the null of a unit root against a nonlinear stationary alternative, capturing adjustment dynamics that the standard linear ADF test misses.

Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359-379.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.

augmented-dickey-fuller-unit-root-test phillips-perron-unit-root-test nonlinear-kpss-test nonlinear-ardl-bounds-test zivot-andrews-structural-break-test nonlinear-vecm


Nonlinear AR Model

NAR model
nonlinear autoregression
NLAR
threshold autoregressive model

Tong, H. (threshold AR); Terasvirta, T. (STAR variant)

The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series.

Univariate time series (continuous, stationary or near-stationary)

Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.

Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208-218.

autoregressive-model arma-model arima-model nonlinear-vecm nonlinear-ardl structural-break-ar-model


Nonlinear ARCH model

Nonlinear Autoregressive Conditional Heteroscedasticity Model

nonlinear conditional heteroscedasticity model

The Nonlinear ARCH (NARCH) model, introduced by Higgins and Bera (1992), extends Engle's original ARCH framework by allowing the power transformation of volatility to be estimated from the data rather than fixed at two. This flexibility captures a broader class of volatility dynamics observed in financial and macroeconomic time series.

Time series (financial returns, inflation, exchange rates)

Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 33(1), 137-158.

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

garch-model arch-model egarch-model gjr-garch-model threshold-arch-model stochastic-volatility-model


Nonlinear ARDL bounds test

Nonlinear Autoregressive Distributed Lag Bounds Test

NARDL
asymmetric ARDL
nonlinear bounds testing approach
NARDL bounds testing

The Nonlinear ARDL bounds test, developed by Shin, Yu, and Greenwood-Nimmo (2014), extends the linear ARDL framework to detect asymmetric long-run relationships in time series. By decomposing a regressor into positive and negative partial sums, NARDL simultaneously tests for cointegration and estimates separate long-run effects for increases and decreases — without requiring all variables to be integrated of the same order.

Time series (levels and first differences)

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.

ardl-bounds-test error-correction-model cointegration-johansen threshold-autoregression asymmetric-error-correction engle-granger-cointegration


Nonlinear ARDL

Nonlinear Autoregressive Distributed Lag Model

NARDL
nonlinear bounds test
asymmetric ARDL
asymmetric cointegration model

The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.

Time series, I(0)/I(1) mixed integration order

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

ardl-bounds-test vector-error-correction-model engle-granger-cointegration-test johansen-cointegration-test granger-causality-test quantile-regression


Nonlinear Arellano-Bond GMM

Nonlinear Arellano-Bond Generalised Method of Moments

nonlinear AB-GMM
dynamic nonlinear panel GMM
nonlinear difference GMM
NL-GMM dynamic panel

Arellano & Bond (1991), extended to nonlinear settings by Wooldridge and others

Nonlinear Arellano-Bond GMM extends the classic Arellano-Bond difference-GMM framework to panel models where the conditional mean function is nonlinear in parameters or variables. It uses lagged levels of the dependent variable as instruments after first-differencing to remove individual fixed effects, yielding consistent estimates in short dynamic panels with nonlinear specifications such as count, duration, or multiplicative models.

Balanced or unbalanced panel data with a lagged dependent variable and potential nonlinearity

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.

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

arellano-bond-gmm system-gmm blundell-bond-gmm dynamic-panel-data nonlinear-panel-models iv-estimation


Nonlinear ARIMA model

Nonlinear Autoregressive Integrated Moving Average Model

nonlinear ARIMA
NARIMA
nonlinear time series model
nonlinear Box-Jenkins model

Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)

The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent.

arima-model setar-model lstar-model garch-model var-model regime-switching-model


Nonlinear ARMA model

Nonlinear Autoregressive Moving Average Model

NARMA
nonlinear ARMA
NLARMA
nonlinear autoregressive moving average

Tong (1990); Granger & Terasvirta (1993)

The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial time series.

Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.

Granger, C. W. J., & Terasvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford University Press.

arma-model threshold-autoregressive-model smooth-transition-autoregressive-model arch-model bilinear-time-series-model neural-network-autoregression


Nonlinear DCC-GARCH model

Nonlinear Dynamic Conditional Correlation GARCH Model

ADCC-GARCH
Asymmetric DCC-GARCH
NL-DCC-GARCH
Nonlinear Asymmetric DCC

The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.

Multivariate volatility and correlation model

Multivariate financial time series (returns)

Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.

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

dcc-garch-model bekk-garch-model egarch-model gjr-garch-model multivariate-garch copula-garch


Nonlinear difference GMM

Nonlinear Difference Generalized Method of Moments

nonlinear diff-GMM
nonlinear Arellano-Bond GMM
first-difference nonlinear GMM
NL-GMM

Wooldridge; building on Arellano and Bond (1991)

Nonlinear Difference GMM extends the Arellano-Bond difference GMM estimator to models where the structural relationship between the outcome and its predictors is inherently nonlinear. By first-differencing to eliminate individual fixed effects and then applying GMM moment conditions with lagged levels as instruments, it consistently estimates parameters in dynamic panel settings without requiring a linear functional form.

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

difference-gmm system-gmm panel-fixed-effects two-stage-least-squares instrumental-variables generalized-method-of-moments


Nonlinear Dynamic Panel Data Model

nonlinear panel with lagged dependent variable

Wooldridge (2005); Honore & Tamer (2006); building on Heckman (1981)

The nonlinear dynamic panel data model extends standard panel methods to settings where the outcome is binary, count-valued, or censored and where past realizations of the outcome directly affect current ones. It handles unobserved individual heterogeneity alongside state dependence, disentangling genuine persistence from spurious persistence driven by unmeasured unit characteristics.

Dynamic panel estimator with nonlinear response

Balanced or unbalanced panel data; binary, count, or censored outcomes

Wooldridge, J. M. (2005). Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics, 20(1), 39-54.

Honore, B. E., & Tamer, E. (2006). Bounds on parameters in panel dynamic discrete choice models. Econometrica, 74(3), 611-629.

arellano-bond-gmm dynamic-panel-data-model random-effects-probit fixed-effects-logit system-gmm panel-fixed-effects


Nonlinear EGARCH model

Nonlinear Exponential Generalized Autoregressive Conditional Heteroscedasticity Model

NL-EGARCH
nonlinear exponential GARCH
asymmetric EGARCH
NEGARCH

The Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely used in financial econometrics to model leverage effects and complex volatility dynamics in asset returns.

Financial time series, high-frequency returns

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

Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48(5), 1749–1778.

egarch-model garch-model tgarch-model gjr-garch-model arch-model stochastic-volatility-model


Nonlinear Engle-Granger Cointegration

Nonlinear Engle-Granger Cointegration Test

nonlinear cointegration
threshold cointegration
KSS cointegration
ESTAR cointegration

Kapetanios, Shin & Snell; Enders & Granger

Nonlinear Engle-Granger cointegration extends the classical two-step Engle-Granger procedure to detect long-run equilibria where adjustment toward the equilibrium is nonlinear — for example, faster above than below a threshold, or governed by a smooth transition mechanism. It is widely applied in financial economics, purchasing power parity tests, and commodity price analysis.

Kapetanios, G., Shin, Y., & Snell, A. (2006). Testing for cointegration in nonlinear smooth transition error correction models. Econometric Theory, 22(2), 279-303.

Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economic Statistics, 16(3), 304-311.

engle-granger-cointegration johansen-cointegration threshold-autoregression ardl-bounds-test error-correction-model nonlinear-ardl


Nonlinear Fixed Effects Model

Nonlinear Fixed Effects Panel Data Model

nonlinear FE model
NLFE
conditional fixed effects model
incidental parameters model

The nonlinear fixed effects model extends fixed effects panel estimation to outcomes governed by nonlinear response functions — such as binary, count, or censored outcomes — while absorbing unobserved individual heterogeneity through unit-specific intercepts. Key special cases include conditional logit for binary outcomes and Poisson fixed effects for count data.

Panel data (balanced or unbalanced); binary, count, or limited-dependent outcomes

Chamberlain, G. (1984). Panel data. In Z. Griliches & M. D. Intriligator (Eds.), Handbook of Econometrics (Vol. 2, pp. 1247–1318). Elsevier.

fixed-effects-model panel-fixed-effects-model random-effects-model nonlinear-random-effects-model panel-data-analysis dynamic-panel-data-model


Nonlinear GARCH model

Nonlinear Generalized Autoregressive Conditional Heteroscedasticity Model

NL-GARCH
asymmetric GARCH
GJR-GARCH
nonlinear volatility model

Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH

The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.

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.

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

arch-model egarch-model tgarch-model dcc-garch-model vector-autoregression arima-model


Nonlinear GLS

NGLS
nonlinear generalized least squares
feasible nonlinear GLS
FNGLS

Gallant (1975); extended by Davidson & MacKinnon

Nonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates.

Cross-sectional, time-series, or panel data with heteroscedastic or autocorrelated errors

Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley.

Davidson, R., & MacKinnon, J. G. (2004). Econometric Theory and Methods. Oxford University Press.

nonlinear-least-squares gls-regression gmm-estimation feasible-gls nliv-estimation seemingly-unrelated-regression


Nonlinear Granger Causality

nonlinear causality test
BDS-based causality
Diks-Panchenko test
nonparametric Granger causality

Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)

Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture.

Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.

Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance, 49(5), 1639-1664.

granger-causality-test toda-yamamoto-causality-test vector-autoregression nonlinear-var-model nonlinear-vecm nonlinear-ardl-bounds-test


Nonlinear Hausman test

Hausman specification test (nonlinear)
nonlinear endogeneity test
Wu-Hausman test (nonlinear)
NL-Hausman test

1978 (nonlinear extension developed through 1980s–1990s)

The Nonlinear Hausman test extends Hausman's (1978) endogeneity specification test to nonlinear models such as probit, logit, Tobit, and count-data regressions. It tests whether suspected regressors are endogenous — i.e., correlated with the error term — in a model where the outcome or the relationship is inherently nonlinear, ensuring that IV-corrected estimates are necessary.

Cross-sectional or panel data with discrete or nonlinear outcomes

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

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

hausman-test instrumental-variables two-stage-least-squares probit-regression logit-regression control-function-approach


Nonlinear Johansen Cointegration

nonlinear cointegration test
threshold Johansen cointegration
rank test for nonlinear cointegration
nonlinear VECM cointegration

Breitung (2001), building on Johansen (1988, 1991)

Nonlinear Johansen cointegration extends the classical Johansen framework to detect long-run equilibrium relationships among integrated time series when the adjustment process is nonlinear. Using rank-based transformations, the approach tests for cointegration without assuming a linear error-correction mechanism, making it suitable for economic relationships characterized by asymmetric or threshold dynamics.

Nonparametric rank-based cointegration test

Multivariate integrated time series (I(1) or I(2))

Breitung, J. (2001). Rank tests for nonlinear cointegration. Journal of Business and Economic Statistics, 19(3), 331-340.

Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580.

johansen-cointegration threshold-cointegration engle-granger-cointegration nonlinear-ardl vector-error-correction-model nardl-bounds-test


Nonlinear KPSS Test

KPSS nonlinearity test
nonlinear stationarity test
flexible Fourier KPSS
NL-KPSS

The nonlinear KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin stationarity test by modelling unknown smooth structural breaks in the deterministic trend using a Fourier approximation. Under the null hypothesis the series is stationary around a flexible nonlinear trend, guarding against spurious unit-root findings caused by regime shifts or gradual transitions.

Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.

Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.

kpss-test adf-test fourier-adf-test nonlinear-unit-root-test smooth-transition-autoregression zivot-andrews-test


Nonlinear MA model

NMA model
nonlinear moving average
NLMA model
nonlinear MA

Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)

The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion.

Univariate time series (equally spaced observations)

Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen.

Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.

arma-model threshold-arma bilinear-model garch-model star-model nonlinear-ar-model


Nonlinear NARDL

NARDL
nonlinear ARDL
asymmetric ARDL
nonlinear bounds test

The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture.

Time series (nonstationary or mixed I(0)/I(1))

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

ardl-bounds-test error-correction-model threshold-cointegration ols-regression granger-causality var-model


Nonlinear OLS

nonlinear least squares
NLS
NLLS
nonlinear regression

Gallant (1987); Wooldridge (2010) for econometric treatment

Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal.

Cross-sectional, time series, or panel; continuous outcome

Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons.

ols-regression generalized-least-squares nonlinear-gls maximum-likelihood-estimation nonlinear-ardl nls-regression


Nonlinear Panel Data Analysis

nonlinear panel models
panel nonlinear econometrics
fixed-effects nonlinear models
random-effects nonlinear models

Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous.

Repeated observations on the same units over time (panel/longitudinal data)

Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.

panel-fixed-effects panel-random-effects logistic-regression probit-regression poisson-regression dynamic-panel-data


Nonlinear PP unit root test

Nonlinear Phillips-Perron Unit Root Test

PP unit root test with nonlinear adjustment

1988 (base); 2000s (nonlinear extensions)

Phillips & Perron (1988); nonlinear extensions by Kapetanios, Shin & Snell (2003) and related authors

The Nonlinear Phillips-Perron unit root test extends the classic PP test by allowing the adjustment toward equilibrium to follow a nonlinear path — such as a smooth transition or threshold mechanism — rather than assuming a constant linear speed of adjustment. This makes it more powerful when the true data-generating process involves regime-dependent or asymmetric mean-reversion dynamics.

Unit root test with nonlinear adjustment

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

Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359-379.

phillips-perron-unit-root-test augmented-dickey-fuller-unit-root-test nonlinear-adf-unit-root-test nonlinear-kpss-test zivot-andrews-structural-break-test nonlinear-ardl


Nonlinear Programming

NLP optimization
Constrained nonlinear optimization
Smooth optimization
Doğrusal olmayan programlama

Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences.

Minimize or maximize a nonlinear function subject to constraints

KKT conditions (first-order necessary optimality)

Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer.


Nonlinear Random Effects Model

nonlinear RE model
NLRE model
random effects nonlinear panel model
mixed nonlinear panel model

Heckman (1981); Chamberlain (1984); further systematized by Wooldridge (2010)

The nonlinear random effects model extends classical random effects estimation to settings where the outcome variable is binary, count-based, censored, or otherwise non-continuously distributed across panel units. It accounts for unobserved individual heterogeneity by treating unit-specific effects as random draws from a distribution, then integrating them out to form a likelihood that can be maximised over the structural parameters.

Balanced or unbalanced panel data; binary, count, censored, or limited dependent variables

random-effects-model fixed-effects-model panel-probit-model panel-tobit-model generalized-linear-mixed-model correlated-random-effects-model


Nonlinear SARIMA Model

Nonlinear Seasonal Autoregressive Integrated Moving Average Model

NL-SARIMA
nonlinear seasonal ARIMA
threshold SARIMA
smooth transition SARIMA

Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications

The Nonlinear SARIMA model extends the classical Seasonal ARIMA framework by replacing the linear conditional mean function with a nonlinear specification — such as threshold switching or smooth transition — while retaining seasonal differencing and lag structure. It is used when seasonal time series exhibit regime-dependent dynamics, asymmetric adjustment, or other nonlinear patterns that a linear model cannot capture.

Univariate seasonal time series (equally spaced observations)

Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.

Franses, P. H., & van Dijk, D. (2000). Non-linear Time Series Models in Empirical Finance. Cambridge University Press.

sarima-model threshold-autoregressive-model smooth-transition-autoregressive-model arima-model seasonal-exponential-smoothing garch-model


Nonlinear SVAR Model

Nonlinear Structural Vector Autoregression Model

nonlinear structural VAR
NL-SVAR
threshold SVAR
regime-switching SVAR

Extensions by Koop, Potter, Auerbach, Gorodnichenko and others

The Nonlinear Structural VAR model extends the standard SVAR framework to allow structural relationships and dynamic responses to vary across economic regimes or states of the world. By imposing nonlinear transition mechanisms — such as threshold switching or smooth regime change — it captures asymmetric responses to shocks that a linear SVAR cannot detect.

Multivariate nonlinear structural time series model

Multivariate time series (macroeconomic, financial)

Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358.

Auerbach, A. J., & Gorodnichenko, Y. (2012). Measuring the output effects of fiscal policy. American Economic Journal: Economic Policy, 4(2), 1–27.

structural-var vector-autoregression nonlinear-var-model nonlinear-vecm vector-error-correction-model nonlinear-ardl


Nonlinear System GMM

Nonlinear System Generalized Method of Moments

nonlinear system generalized method of moments

Nonlinear System GMM extends the Generalized Method of Moments framework to estimate a system of structural equations in which the parameter vector enters the moment conditions nonlinearly. It jointly exploits moment restrictions across multiple equations, yielding efficiency gains over single-equation approaches when the equations share parameters or have correlated disturbances.

Cross-sectional, panel, or time-series data with nonlinear moment conditions

Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054.

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

linear-system-gmm gmm-estimation two-stage-least-squares nonlinear-least-squares instrumental-variables panel-fixed-effects


Nonlinear TGARCH model

NL-TGARCH
Nonlinear Threshold GARCH
Asymmetric TGARCH
GJR-GARCH variant

Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle

The Nonlinear TGARCH (Threshold GARCH) model extends the standard GARCH framework by allowing positive and negative shocks of equal magnitude to exert different effects on future volatility. It models conditional volatility in terms of the absolute value of lagged residuals split by a sign threshold, capturing the well-documented leverage effect in financial return series.

High-frequency or daily financial time series

Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.

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 gjr-garch-model tgarch-model aparch-model arch-model


Nonlinear Time-History Analysis

Nonlinear Time-History Analysis for Seismic Response

Nonlinear dynamic analysis
Step-by-step integration
Time domain analysis

Nonlinear time-history analysis is a numerical method that solves the equations of motion step-by-step in the time domain, using recorded or synthetic earthquake ground motions as input. Developed by Newmark in 1959, this approach captures the full dynamic response of structures including material nonlinearity, geometric effects, and energy dissipation mechanisms.

Time-stepping numerical method for earthquake engineering

Newmark, N. M. (1959). A method of computation for structural dynamics. Journal of the Engineering Mechanics Division, 85(3), 67-94.

Clough, R. W., & Penzien, J. (1993). Dynamics of Structures (2nd ed.). McGraw-Hill.

Hilber, H. M., Hughes, T. J., & Taylor, R. L. (1976). Improved numerical dissipation for time integration algorithms in structural dynamics. Earthquake Engineering & Structural Dynamics, 5(3), 283-292.

pushover-analysis response-spectrum-analysis incremental-dynamic-analysis equivalent-static-analysis


Nonlinear Toda-Yamamoto Causality

Nonlinear Toda-Yamamoto Granger Causality Test

nonlinear TY causality
rank-based Toda-Yamamoto test
modified Wald nonlinear causality
NTY causality test

1995 (base); nonlinear extensions 2000s–2010s

Toda & Yamamoto (1995) for the linear base; nonlinear extension developed by subsequent researchers applying rank transformations or neural-network-augmented VAR

The Nonlinear Toda-Yamamoto causality test extends the classic Toda-Yamamoto (1995) modified Wald procedure to detect causal linkages that are hidden in the means of series but manifest through nonlinear dynamics such as asymmetries, threshold effects, or volatility transmission. It fits an augmented VAR on rank-transformed or otherwise nonlinearly mapped series and applies a chi-squared Wald test on the extra-lag coefficients.

Time series (univariate or multivariate, possibly nonstationary)

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

Sims, C. A., Stock, J. H., & Watson, M. W. (1990). Inference in linear time series models with some unit roots. Econometrica, 58(1), 113-144.

toda-yamamoto-causality granger-causality bds-test nonlinear-granger-causality var-model cointegration-test


Nonlinear VAR Model

NLVAR
nonlinear vector autoregression
threshold VAR
TVAR

Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework

The Nonlinear VAR (NLVAR) model extends the standard vector autoregression by allowing the dynamic relationships among multiple time series to switch or change smoothly depending on an observed threshold variable, a latent regime state, or a smooth transition function. It is used when economic systems exhibit asymmetric responses, regime shifts, or state-dependent dynamics that a linear VAR cannot capture.

Multivariate nonlinear time series model

Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202.

Krolzig, H.-M. (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis. Springer.

vector-autoregression structural-var vector-error-correction-model nonlinear-ardl markov-switching-model threshold-autoregression


Nonlinear VECM

nonlinear VECM
NVECM
threshold VECM
asymmetric VECM

Granger & Lee (1989); Enders & Granger (1998)

The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss.

Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311.

Granger, C. W. J., & Lee, T. H. (1989). Investigation of production, sales and inventory relationships using multicointegration and non-symmetric error correction models. Journal of Applied Econometrics, 4(S1), S145–S159.

vecm threshold-cointegration smooth-transition-var markov-switching-var ardl-bounds-test johansen-cointegration


Nonlinear WLS

1960s–1980s (formalized in applied econometrics)

Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting

Nonlinear Weighted Least Squares combines the flexibility of nonlinear regression with the variance-stabilizing power of observation-level weights. It minimises a weighted sum of squared residuals around a user-specified nonlinear mean function, making it the method of choice when the relationship is inherently nonlinear and error variance differs across observations.

Cross-sectional, time-series, or panel data with a nonlinear mean function and heteroscedastic errors

Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education.

Bates, D. M., & Watts, D. G. (1988). Nonlinear Regression Analysis and Its Applications. John Wiley & Sons.

nonlinear-least-squares weighted-least-squares generalized-least-squares feasible-gls ols-regression heteroscedasticity-consistent-estimator


Nonlinear Zivot-Andrews test

nonlinear structural break unit root test

Zivot-Andrews test with nonlinear adjustment

Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors

The Nonlinear Zivot-Andrews test extends the classical Zivot-Andrews structural-break unit root test by embedding smooth-transition nonlinear adjustment into the test regression. It jointly searches for an endogenous structural break and allows the speed of mean-reversion to vary with distance from the attractor, producing more power against nonlinear stationary alternatives than either test alone.

Unit root test with structural break and nonlinear adjustment

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.

Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359–379.

zivot-andrews-test kapetanios-shin-snell-test augmented-dickey-fuller-test lm-unit-root-test lee-strazicich-test smooth-transition-regression


Nonparametric Statistical Tests

rank-based tests
Mann-Whitney U
Kruskal-Wallis
distribution-free

Nonparametric (distribution-free) tests are statistical methods for hypothesis testing that do not assume data follow a specific probability distribution (e.g., normal), making them robust to departures from normality, outliers, and ordinal data. The Mann-Whitney U test (1947) and Kruskal-Wallis test (1952) extend hypothesis testing beyond the constraints of parametric assumptions. Essential in biology, medicine, psychology, and any field where data are non-normal, highly skewed, or measured on ordinal scales (rankings, ratings), nonparametric tests provide valid inference when parametric assumptions fail.

Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.

Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). John Wiley & Sons.


Non-stationary Transformer

Non-stationary Transformers for Forecasting

NS-Transformer
Non-stationary Transformer Network
Stationarization-based Transformer
Durağan-Olmayan Transformer

Non-stationary Transformer is a Transformer-based time-series forecasting architecture introduced by Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long at NeurIPS 2022. It addresses a fundamental tension in applying Transformers to real-world time series: over-stationarization during preprocessing strips out non-stationary signals that carry predictive information, while raw non-stationary inputs cause attention to collapse. The model resolves this through series stationarization paired with a novel de-stationary attention mechanism that restores the original temporal distribution in predictions.

Transformer-based time-series forecasting model

Series stationarization with de-stationary attention

Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. NeurIPS.


NORM-VECTOR

NORM-VECTOR (Vector Normalization — Euclidean column-norm scaling (L2 normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Normalization (L2, unit-sphere projection)

Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag


NPT

NPT
Normalization Process Theory
NPT Framework
Normalisation Process Theory

Carl R. May, PhD; Elena Murray, PhD; and colleagues at University of Sydney and UCL

Normalization Process Theory (NPT) is a framework developed by May, Murray, and colleagues (2009) to explain how new practices, technologies, and innovations become embedded and sustained in everyday organizational and clinical work. Rather than viewing implementation as a one-time adoption event, NPT conceptualizes implementation as a process of normalization—the gradual transition from 'new and unusual' to 'normal, routine work integrated into standard processes.' NPT identifies four normalization mechanisms: Coherence (shared understanding of the intervention's purpose and value), Cognitive Participation (staff engagement and involvement in learning and using the intervention), Collective Action (the work required to implement, including workflow changes and resource allocation), and Reflexive Monitoring (ongoing reflection on impacts, benefits, and needed adaptations). NPT has become influential in implementation science research, particularly in health technology implementation and complex intervention studies, and provides a theoretical lens for understanding why some innovations become normalized while others are abandoned.

Theoretical framework with qualitative and mixed-methods assessment

Murray, E., Treweek, S., Pope, C., MacFarlane, A., Ballini, L., Dowrick, C., ... & May, C. R. (2010). Normalizing adoption of new health care innovations: A systematic review of empirical studies. American Journal of Health Promotion, 24(4), e5–e15.

May, C. R., Murray, E., Mair, F. S., & Finch, T. (2009). Development of a theory of implementation and integration of digital innovations in health and social care: The Normalization Process Theory. Inform Prim Care, 17(2), 89–99.

knowledge-to-action-scale evidence-based-practice-attitude implementation-climate-scale stages-of-concern-questionnaire organisational-readiness-change


Normalization Process Theory

Normalization Process Theory (NPT): A Sociological Framework for Understanding How New Interventions Become Routinely Embedded in Practice

Normalization Process Theory (NPT) is a sociological framework developed by Carl May and colleagues to explain how new interventions become routinely embedded ('normalized') in organizational and clinical practice. Unlike efficiency-focused frameworks that measure adoption and fidelity, NPT explains the social processes through which interventions transition from external innovations to normal practice. NPT identifies four key mechanisms (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring) that collectively determine whether an intervention becomes 'the way we do things here' or remains a temporary project.

May, C. R. (2006). A rational model for assessing and evaluating complex interventions in health care. BMC Health Services Research, 6, 86.

May, C. R., & Finch, T. (2009). Implementing, embedding, and integrating practices: An outline of normalization process theory. Sociology, 43(3), 535-554.

Murray, E., Treweek, S., Pope, C., MacFarlane, A., Ballini, L., Dowrick, C., ... & Vanoli, A. (2010). Normalizing intervention: Developing and validating a tool to assess implementation fidelity of complex interventions using NOMAD (NormalizatiOn: Measure, Assess, Develop). Implementation Science, 5, 78.

cfir-framework implementation-outcome-taxonomy behavior-change-wheel knowledge-translation fidelity-assessment


Normalized Mutual Information

Normalized Mutual Information for Clustering Agreement

NMI
mutual information
information criterion

Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect agreement, while 0 indicates independence.

Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.

adjusted-rand-index fowlkes-mallows-index v-measure silhouette-score davies-bouldin-index


Normalizing Flows

Flow-Based Generative Models
Invertible Neural Networks
Exact Likelihood Models
Akışa Dayalı Üretici Modeller

Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable exact likelihood computation and efficient sampling, making them a principled alternative to VAEs and GANs for density estimation and generation tasks.

Generative model via invertible transformations

Exact maximum likelihood via change-of-variables

Rezende, D. J., & Mohamed, S. (2015). Variational inference with normalizing flows. International Conference on Machine Learning (ICML), 1530–1538.


Norton Scale

Norton Scale for Pressure Sore Risk Assessment

Doreen Norton, Rhonda McLaren, and A. N. Exton-Smith

The Norton Scale is a pioneering risk assessment tool developed by Doreen Norton and colleagues in 1962 to identify hospitalized patients at risk of developing pressure sores. As one of the earliest standardized pressure ulcer risk assessment instruments, the Norton Scale predates and influenced many later tools including the widely used Braden Scale. It remains relevant in clinical practice, particularly in geriatric and long-term care settings.

Norton, D., McLaren, R., & Exton-Smith, A. N. (1962). An investigation of geriatric nursing problems in hospital. National Corporation for the Care of Old People, London.

Bergstrom, N., Demuth, P. J., & Braden, B. J. (1987). A clinical trial of the Braden Scale for predicting pressure sore risk. Nursing Clinics of North America, 22(2), 417-428.

braden-scale patient-fall-risk-assessment care-dependency-scale wound-assessment-bates-jensen


NOSE

Nasal Obstruction Symptom Evaluation Scale

Mark G. Stewart, David L. Witsell, Thomas L. Smith, and colleagues

The Nasal Obstruction Symptom Evaluation (NOSE) Scale is a brief 5-item self-report questionnaire specifically designed to measure the severity of nasal obstruction and its impact on quality of life. Developed by Stewart and colleagues (2004), the NOSE is the most widely used nasal obstruction-specific outcome measure in otolaryngology, recommended for clinical practice and clinical trials. It is validated for baseline assessment, monitoring treatment response in medical and surgical rhinology, and outcome evaluation following septoplasty, rhinoplasty, endoscopic sinus surgery, or allergen immunotherapy.

Stewart, M. G., Witsell, D. L., Smith, T. L., Weaver, E. M., Yueh, B., & Hannley, M. T. (2004). Development and validation of the Nasal Obstruction Symptom Evaluation (NOSE) Scale. Otolaryngology - Head and Neck Surgery, 130(2), 157-163.


NoSQL Schema Design

NoSQL Database Schema Design and Data Modeling

NoSQL community pioneers (various founders)

NoSQL schema design is the practice of organizing data for non-relational databases optimized for specific access patterns and scale. Unlike relational design which normalizes data to eliminate redundancy, NoSQL design often embraces denormalization, embedding, and duplicate data to optimize query performance in distributed systems.

Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12-27.

Chodorow, K. (2013). MongoDB: The Definitive Guide (2nd ed.). Sebastopol, CA: O'Reilly.

Sadalage, P. J., & Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley.

document-databases key-value-stores denormalization embedding-vs-references indexing-strategies


NOTEARS

NOTEARS Continuous DAG Structure Learning

DAGs with NO TEARS
Continuous Structure Learning
Continuous DAG Optimization
Sürekli DAG Yapı Öğrenimi

NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization problem, enabling the use of standard gradient-based solvers and removing the need for exhaustive combinatorial search over graph space.

Continuous optimization algorithm for causal DAG discovery

Differentiable algebraic h(W) = tr(e^{W \circ W}) - d = 0

Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31.


Nottingham Health Profile

Nottingham Health Profile Assessment Scale

Stephen Hunt and colleagues at University of Nottingham

The Nottingham Health Profile (NHP) is a perceived health status measure developed by Hunt and colleagues at the University of Nottingham in 1981. It measures subjective well-being across six dimensions: physical mobility, pain, sleep, emotional reactions, social isolation, and energy level. The NHP emphasizes the patient's experience of health problems rather than objective clinical measures.

Hunt, S. M., McKenna, S. P., McEwen, J., et al. (1985). The Nottingham Health Profile: subjective health status and medical consultations. Social Science & Medicine, 21(3), 347–354.

Hunt, S. M., McEwen, J., & McKenna, S. P. (1981). Measuring health status: a new tool for clinicians and epidemiologists. Journal of the Royal College of General Practitioners, 31(228), 377–384.

McEwen, J., & Hunt, S. M. (2003). Measurement of functional status and well-being. In S. M. Sutherland et al. (Eds.), Health and Health Care in Britain: Text and Applications. Macmillan.


NAS-PI

NAS-PI
Novaco Anger Scale
Provocation Inventory

The Novaco Anger Scale and Provocation Inventory (NAS-PI) is a comprehensive self-report assessment instrument developed by Raymond Novaco (2003) to measure dispositional anger and anger provocation in adolescents and adults. It integrates cognitive-behavioral theory of anger and emotional regulation, serving clinicians, forensic practitioners, and researchers in psychiatric, correctional, and clinical settings where anger dysregulation and aggression risk are clinical concerns.

Novaco, R. W. (2003). The Novaco Anger Scale and Provocation Inventory (NAS-PI): Professional manual. Psychological Assessment Resources, Inc.

Novaco, R. W. (1994). Anger as a risk factor for violence among the mentally disordered. In J. Monahan & H. J. Steadman (Eds.), Violence and mental disorder: Developments in risk assessment (pp. 21–59). University of Chicago Press.

hcr-20 beck-hopelessness-scale psychopathy-checklist-screening level-of-service-inventory


NRS-2002 Nutritional Risk Screening

The Nutritional Risk Screening 2002 (NRS-2002), developed by Kondrup et al. and endorsed by ESPEN (European Society for Parenteral and Enteral Nutrition), is a 7-point tool for identifying hospitalized patients at nutritional risk. It combines assessment of recent weight loss, dietary intake, disease severity, and age to stratify the need for nutritional intervention.

Nutritional status and intervention need

Kondrup, J., Allison, S. P., Elia, M., Vellas, B., & Plauth, M. (2003). ESPEN guidelines for nutrition screening 2002. Clinical Nutrition, 22(3), 415-421.

Kondrup, J., Rasmussen, H. H., Hamberg, O., Stadshaug, A., & Ad Hoc ESPEN Working Group. (2003). Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clinical Nutrition, 22(3), 321-336.


NSGA-III

NSGA-III algorithm
NSGA-III evolutionary
many-objective optimization

NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives.

Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.

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


NSGA-II

NSGA-II — Çok Amaçlı Evrimsel Optimizasyon

NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.

Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan

Evolutionary multi-objective optimisation algorithm

Non-dominated sorting + crowding distance

Output: Pareto-optimal front (set of trade-off solutions)

Continuous, binary, or categorical decision variables

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.

Zitzler, E., Deb, K. & Thiele, L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2), 173-195.

differential-evolution genetic-algorithm ant-colony-optimization particle-swarm-optimization pareto-analysis


NSS-CODAS

NSS-CODAS (Neutrosophic Spherical extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Bhuvaneshwari, S., Antony Crispin Sweety, C. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Neutrosophic Spherical Set (NSS) outranking — T,I,F ∈ [0,1] independent with squared-norm bound 0 ≤ T²+I²+F² ≤ √3 (Bhuvaneshwari-Sweety 2024 Eq.(2)); combined with CODAS Euclidean-distance ranking

Bhuvaneshwari, S., Antony Crispin Sweety, C. (2024). Neutrosophic Spherical Sets in MCDM. Neutrosophic Sets and Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Nuclear Decay Analysis

Nuclear Decay Analysis and Radioactive Process Evaluation

decay kinetics
radioactive decay modeling
half-life analysis

Nuclear decay analysis is the systematic study of radioactive transformation processes, originating from Rutherford and Soddy's work in the early 1900s. It quantifies the rate and modes of nuclear disintegration using decay constants, half-lives, and branching ratios to predict activity evolution, date samples via radiometric methods, and assess the long-term hazard from radioactive materials.

Evans, R. D. (1955). The Atomic Nucleus. McGraw-Hill.

Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.

activation-analysis dosimetry-measurement radioactive-waste-classification reactor-kinetics radiation-dose-assessment


Nuclear Fuel Cycle Analysis

Nuclear Fuel Cycle Analysis and Material Flow Assessment

fuel cycle modeling
material accounting
energy lifecycle assessment

Nuclear fuel cycle analysis is a comprehensive assessment of uranium and plutonium flows from extraction through enrichment, power generation, and waste management, originating from Fermi's controlled nuclear reaction. It quantifies resource requirements, energy balances, greenhouse gas emissions, and waste streams to evaluate nuclear energy sustainability, proliferation risk, and economic viability.

system-level material and energy accounting

International Atomic Energy Agency (2021). Nuclear Fuel Cycle Information System (NFCIS). IAEA-NDS-3/Rev.2.

Cochran, T. B., Paine, C. E., Feiveson, H. A., & von Hippel, F. N. (2010). Fast Breeder Reactor Development in the U.S.: A Comparative Failure. Oxford University Press.

nuclear-decay-analysis criticality-safety-analysis reactor-kinetics radiation-dose-assessment radioactive-waste-classification


Nucleophilic Substitution Analysis

Nucleophilic Substitution Reaction Analysis

Nucleophilic substitution reaction analysis is the systematic study of how nucleophiles attack electrophilic carbons (or other atoms), displacing leaving groups and forming new bonds. Formalized by Hughes, Ingold, and Winstein from the 1930s onward, this framework distinguishes mechanistic pathways (SN1 vs. SN2) and enables chemists to predict outcomes, optimize conditions, and design synthetic routes using substitution reactions.

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.

Winstein, S., & Grunwald, E. (1955). The correlation of solvolysis rates. III. t-butyl chloride in a wide range of solvent mixtures. Journal of the American Chemical Society, 77(12), 3191–3207.


Nudged Elastic Band Method

NEB
elastic band method
transition-path finding

The Nudged Elastic Band (NEB) method is a computational technique for finding minimum-energy transition paths between stable atomic configurations and estimating activation barriers. Developed by Jónsson, Mills, and Jacobsen in 1998, NEB connects initial and final states with a chain of images (configurations) held together by artificial springs, then optimizes the chain to trace a reaction pathway. The climbing-image variant, introduced by Henkelman in 2000, further refines the saddle point. NEB is the standard tool in materials science and chemistry for modeling diffusion, defect formation, and chemical reactions at the atomic scale.

Jonsson, H., Mills, G., & Jacobsen, K. W. (1998). Nudged elastic band method for finding minimum energy paths of transitions. Classical and Quantum Dynamics in Condensed Phase Simulations. World Scientific.

Henkelman, G., Uberuaga, B. P., & Jonsson, H. (2000). A climbing image nudged elastic band method for finding saddle points and minimum energy paths. The Journal of Chemical Physics, 113(22), 9901-9904.

Sheppard, D., Terrell, R., & Henkelman, G. (2008). Optimization methods for finding minimum energy paths. The Journal of Chemical Physics, 128(13), 134106.


Null Hypothesis Testing

Null Hypothesis Significance Testing (NHST) and Hypothesis Formulation

NHST
hypothesis formulation
null hypothesis
alternative hypothesis
two-tailed test

Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.

Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337.

Gigerenzer, G., & Marewski, J. N. (2015). Surrogate Science: The Idol of a Universal Method for Scientific Inference. Journal of Management, 41(2), 421–440.

p-value-significance type-i-type-ii-error statistical-power confidence-interval


Number of Identified Specimens

Number of identified specimens (NISP) is a fundamental zooarchaeological method that quantifies the abundance of faunal remains by counting all identifiable bone fragments or specimens in an assemblage. Formalized by R. E. Chaplin and later refined by Donald Grayson and others, NISP is the most straightforward and widely used quantification metric in zooarchaeology. Despite its simplicity, NISP is sensitive to both cultural and taphonomic factors that affect preservation, fragmentation, and identification of bone assemblages.

Chaplin, R. E. (1971). The Study of Animal Bones from Archaeological Sites. Seminar Press.

Grayson, D. K. (1984). Quantitative Zooarchaeology. Academic Press.

Lyman, R. L. (2008). Quantitative Paleozoology. University of Chicago Press.

minimum-number-of-individuals geometric-morphometrics use-wear-analysis dental-microwear-texture-analysis


Numeric Rating Scale for Pain

The Numeric Rating Scale (NRS) is a single-item, self-report measure of pain intensity developed by Jensen and colleagues in 1986. Patients rate their pain on an 11-point scale (0-10) where 0 represents no pain and 10 represents the worst pain imaginable. The NRS is among the most widely used pain severity measures in clinical practice and research due to its simplicity, rapid administration, and robust measurement properties.

Unidimensional pain severity measurement

Jensen, M. P., Karoly, P., & Braver, S. (1986). The measurement of clinical pain intensity: a comparison of six methods. Pain, 27(3), 297-307.

Jensen, M. P., Turner, J. A., & Romano, J. M. (2001). Changes in beliefs, catastrophizing, and coping are associated with improvement in multidisciplinary pain treatment. Journal of Consulting and Clinical Psychology, 69(4), 655-662.

Hawker, G. A., Mian, S. O., Kendzerska, T., & French, M. (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS), Numeric Rating Scale for Pain (NRS), McGill Pain Questionnaire (MPQ). Arthritis Care & Research, 63(S11), S240-S252.


Nuremberg Code

Nuremberg Code of Ethics for Human Experimentation

International Military Tribunal at Nuremberg (Allied Powers)

The Nuremberg Code (1947) is the first international ethical code governing human experimentation, established by the International Military Tribunal at Nuremberg following trials of Nazi physicians for conducting torture and unethical experiments on concentration camp prisoners. Its ten principles, led by absolute requirement for voluntary informed consent, became the foundation for all modern research ethics governance and remain the gold standard for protecting research subjects from exploitation and abuse.

Nuremberg Military Tribunal. (1947). Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10. United States Government Printing Office.

National Institutes of Health. (2017). The Nuremberg Code. Office of History.

declaration-of-helsinki belmont-report informed-consent-research research-misconduct


Ellen T. Lake (University of Pennsylvania School of Nursing), based on foundational work by Kramer and Hafner (1989)

The Practice Environment Scale of the Nursing Work Index (PES-NWI) is a 31-item instrument designed to measure nurses' perceptions of their practice environment, particularly factors related to autonomy, control over practice, and organizational support. Developed by Lake in 2002 and based on foundational work by Kramer and Hafner, the PES-NWI assesses five key domains: nursing foundations for quality care, staffing and resource adequacy, collegial nurse–physician relationships, nurse manager ability and support, and organizational support for nursing. It is widely used in hospital quality and nursing research to identify environmental factors associated with nurse satisfaction, retention, and patient safety outcomes.

Lake, E. T. (2002). Development of the Practice Environment Scale of the Nursing Work Index. Research in Nursing & Health, 25(3), 176–188.

Aiken, L. H., Sloane, D. M., Lake, E. T., Sochalski, J., & Weber, A. L. (2008). The Practice Environment Scale of the Nursing Work Index: Five country comparison. Nursing Research, 46(5), 298–308.

Lake, E. T. (2007). The nursing practice environment: measurement and evidence. Medical Care Research and Review, 64(2 Suppl), 104S–122S.

safety-attitudes-questionnaire hospital-survey-patient-safety patient-safety-climate-scale healthcare-teamwork-scale


NCCS

Clinical Competence Scale
Nursing Skills Assessment
Competency Rating Scale

The NCCS is a multidimensional self-assessment and clinician-rated instrument measuring nursing students' perceived and observed clinical competence across technical, interpersonal, and cognitive domains. Developed by Walt and van der Walt in 2009, the scale evaluates students' mastery of fundamental nursing skills, critical thinking, communication, and professional judgment. It is used in nursing education to monitor competence development, identify learning gaps, and predict readiness for licensure examinations (e.g., NCLEX-RN).

Walt, R. & van der Walt, C. (2009). The nursing clinical competence scale: Development and psychometric testing of a self-assessment instrument. Nurse Educ Today 29(6): 610–616.

Kostovich, C. T. & Oswald, S. L. (2016). An exploratory study of valid mechanisms for evaluating NCLEX-RN readiness in undergraduate nursing students. Nurse Educ Today 36(1): 375–381.


Nursing-Sensitive Indicators

Nursing-Sensitive Quality Indicators for Healthcare Performance

Quality measurement and performance evaluation

Nursing-Sensitive Indicators are quality metrics that measure healthcare outcomes significantly influenced by nursing care. Developed by the American Nurses Association (ANA) and maintained through the National Database of Nursing Quality Indicators (NDNQI), these indicators assess hospital-acquired complications, staffing levels, nurse-sensitive outcomes, and other dimensions of care quality. They serve as benchmarking tools for evaluating nursing practice effectiveness and organizational performance.

American Nurses Association. (2001). National Database of Nursing Quality Indicators (NDNQI). Journal of Nursing Administration, 31(5), 255-260.

Needleman, J., Buerhaus, P., Paucek, K., Leibson, C. L., & Chiang, Y. P. (2011). The value of nurse staffing in hospitals. Medical Care, 49(2), 249-256.

braden-scale cam-delirium-screening nursing-workload-nems early-warning-score patient-fall-risk-assessment


Nursing Workload NEMS

NEMS
Nursing Workload Score
Intensive Care Workload Assessment

Workload measurement and staffing assessment

Dick R. Miranda, Rui Moreno, and Guido Iapichino

The Nine Equivalents of Nursing Manpower Use Score (NEMS) is a validated assessment instrument specifically designed to quantify nursing workload in intensive care unit (ICU) settings. Developed by Miranda, Moreno, and Iapichino, NEMS measures the intensity of nursing care required based on therapeutic interventions and patient monitoring activities. Unlike severity of illness scores that focus on mortality risk, NEMS directly measures the nursing effort expended, making it particularly useful for ICU staffing decisions and workload documentation.

Miranda, D. R., Moreno, R., & Iapichino, G. (1997). Nine equivalents of nursing manpower use score (NEMS). Intensive Care Medicine, 23(7), 760-765.

Cullen, D. J., Civetta, J. M., Briggs, B. A., & Ferrara, L. C. (1974). Therapeutic Intervention Scoring System (TISS): A method for quantitative comparison of patient acuity. Critical Care Medicine, 2(2), 57-60.

nursing-sensitive-indicators care-dependency-scale early-warning-score patient-fall-risk-assessment


NLAI

Nutrition Literacy Assessment Instrument

NLAI
Nutrition Literacy Assessment
Nutrition Literacy

Diamond; Rothman; Nutrition Literacy Research

The NLAI is a 26-item validated instrument measuring nutrition literacy—the ability to understand nutrition information and use it to make healthy food choices. Developed by Diamond and refined through validation studies by Rothman and colleagues, the NLAI evaluates comprehension of nutrition labels, understanding of portion sizes, knowledge of daily values, and ability to interpret nutrient claims. Nutrition literacy is a critical determinant of dietary quality and health outcomes, particularly in populations with low general literacy or limited numeracy.

Validated questionnaire; knowledge and comprehension

Diamond, M. R. (2007). Development of a reliable and valid nutrition literacy assessment instrument. Journal of Nutrition Education and Behavior, 39(Suppl 5), S26–S30.

Rothman, R. L., Housam, R., Weiss, H., et al. (2006). Patient understanding of food labels: the role of literacy and numeracy. American Journal of Preventive Medicine, 31(5), 391–398.


DASES

Diabetes Self-Efficacy Scale / Nutrition Self-Efficacy Scale

Kate Lorig, Philip L. Ritter, Farrokh Alavifard (Stanford Patient Education Center)

The Nutrition Self-Efficacy Scale, sometimes called the Diabetes Self-Efficacy Scale (DASES), is an 8-item instrument measuring confidence in performing diet-related behaviors and self-management skills. Developed by Lorig and colleagues at the Stanford Patient Education Center in 2003, it is based on self-efficacy theory and measures respondents' confidence in their ability to eat healthily, manage portions, choose healthful foods, and overcome dietary barriers. The scale is used in diabetes care, weight management, and general nutrition intervention research.

Lorig, K., Ritter, P. L., Villa, F., & Piette, J. D. (2009). Spanish language diabetes self-management with and without automated telephone reinforcement: two randomized trials. Diabetes Care, 32(3), 408-414.

Stanford Patient Education Center. (2003). Chronic Disease Self-Efficacy Scales. Stanford University School of Medicine.

mini-nutritional-assessment dietary-quality-index mediterranean-diet-adherence intuitive-eating-scale dutch-eating-behavior-questionnaire


NVivo and ATLAS.ti for Qualitative Analysis

CAQDAS
QDA software
qualitative analysis software
NVivo
ATLAS.ti

QSR International (NVivo) and Scientific Software-Citational (ATLAS.ti)

NVivo and ATLAS.ti are Computer-Assisted Qualitative Data Analysis Software (CAQDAS) programs that facilitate coding, organizing, and analyzing qualitative data—including text (transcripts, documents), images, video, and audio. NVivo, developed by QSR International, is widely used in academic research and supports data organization, coding, memo-writing, retrieval, and analysis visualizations. ATLAS.ti, developed by Scientific Software-Citational, emphasizes hermeneutic interpretation and network visualization. Both tools were introduced in the late 1990s and have become standard across disciplines. CAQDAS is not analysis itself—the researcher must make analytical decisions—but rather augments human analysis by managing large data volumes, organizing codes systematically, tracking analysis decisions, and generating visualizations. These tools improve transparency and rigor in qualitative research.

Lewins, A., & Silver, C. (2007). Using Software in Qualitative Research: A Step-by-Step Guide. SAGE Publications.

Friese, S. (2019). Qualitative Data Analysis with ATLAS.ti (3rd ed.). SAGE Publications.

Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with NVivo (2nd ed.). SAGE Publications.

Hoover, S. M., Polasek, D. W., & Shuart-Faris, N. (2012). Qualitative software and rigor in systematic reviews. Journal of Electronic Resources in Medical Libraries, 9(3), 194-204.

document-analysis qualitative-synthesis-methods saturation-in-qualitative participant-observation


Object-Based Image Analysis

Geographic Object-Based Image Analysis
GEOBIA
Object-Oriented Image Analysis
Nesne Tabanlı Görüntü Analizi

Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery.

Image segmentation and classification pipeline

Input: High-resolution raster imagery (multispectral, hyperspectral, LiDAR)

Output: Semantically labeled image objects (polygons)

Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.


Object Detection

Object Detection (Region-Based and Anchor-Free Deep Neural Network Models)

visual object detection
image object localization
region-based object detection
bounding-box detection

Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)

Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.

Supervised deep learning (region proposal or single-shot)

Images (annotated with bounding boxes and class labels)

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.

image-classification semantic-segmentation instance-segmentation convolutional-neural-network fine-tuned-object-detection transformer


Obsidian Hydration Dating

Obsidian hydration dating (OHD) is a chronometric method that determines the age of obsidian artifacts by measuring the thickness of a hydration layer formed on their exposed surfaces. Developed by Irving Friedman and Robert Smith in 1960, it is based on the principle that fresh obsidian surfaces absorb water from the surrounding environment at a measurable rate. The method is particularly valuable in archaeology for dating volcanic glass tools and other obsidian objects, especially in regions where obsidian was commonly used for cutting and scraping implements.

Friedman, I., & Smith, R. L. (1960). A new dating method using obsidian: Part 1, the surface rind method. Journal of Geophysical Research, 65(4), 1287-1291.

Ericson, J. E., & Berger, R. (1975). Kinetic energy of ionizing radiation as a determinant of the obsidian hydration rate. Nature, 254(5496), 55-56.

Mazer, J. J., Bates, J. K., Stevenson, C. M., & Clelland, J. G. (1991). Obsidian-water reactions: Measuring extremely low water contents in obsidian. Geochimica et Cosmochimica Acta, 55(2), 395-405.


Occlusal Analysis

occlusion assessment
bite analysis
centric relation recording

1899 (Angle's classification); 1950s+ (modern analysis)

Multiple innovators (Angle, Posselt, Dawson, Okeson)

Occlusal analysis is a systematic clinical and instrumental examination that evaluates the relationships between the maxillary and mandibular teeth, the temporomandibular joint, and the muscles of mastication. Comprehensive occlusal analysis informs diagnosis of malocclusion, temporomandibular disorders, and guides prosthodontic and orthodontic treatment planning. The analysis integrates static occlusal relationships (centric relation, centric occlusion) with dynamic occlusal patterns (jaw movements) to assess functional harmony and identify occlusal interferences.

Okeson, J. P. (2020). Management of temporomandibular disorders and occlusion (8th ed.). Elsevier.

Rugh, J. D., & Harlan, J. (1988). Nocturnal masseter muscle activity and symptoms of temporomandibular disorders. Journal of Prosthodontic Research, 60(4), 329-334.

Ash, M. M., & Nelson, S. J. (2003). Wheeler's dental anatomy, physiology and occlusion (8th ed.). WB Saunders.

temporomandibular-joint-analysis orthodontic-cephalometry tooth-mobility-assessment periodontal-probing


Occupational Exposure Questionnaire

NIOSH; Occupational Epidemiology Community

The Occupational Exposure Questionnaire (OEQ) systematically documents workers' exposure to physical, chemical, biological, ergonomic, and psychosocial hazards in their occupational roles. Used by occupational health practitioners and researchers, the OEQ captures frequency, duration, and intensity of hazard exposure, enabling identification of high-risk workers, validation of job exposure matrices, and epidemiological investigation of occupational disease. The OEQ is foundational for occupational health surveillance and regulatory compliance (OSHA, EPA, HSE standards).

National Institute for Occupational Safety and Health (NIOSH). (2007). Exposure assessment: A handbook for conducting occupational health surveys. DHHS (NIOSH) Publication No. 2007-154.

Checkoway, H., Pearce, N., & Kriebel, D. (1989). Research methods in occupational epidemiology. Oxford University Press.


The Occupational Fatigue Exhaustion Recovery Scale (OFER) measures worker fatigue across three dimensions: acute fatigue (tiredness after the current work period), chronic fatigue (accumulated exhaustion over weeks or months), and inter-shift recovery (ability to recuperate between work shifts). Developed by Winwood and colleagues, the OFER distinguishes between short-term fatigue (recoverable) and long-term exhaustion (requiring intervention), making it essential for identifying workers at risk of injury, burnout, and occupational health decline in high-demand roles.

Winwood, P. C., Bakker, A. B., & Winwood, L. M. (2006). Do the effort–reward imbalance model and the demand control model measure occupational fatigue? A claims analysis of occupational health data. J Occup Environ Med, 48(11), 1112–1120.

Winwood, P. C., Winwood, L. M., & Liss-Malone, S. (2007). Development and validation of a scale to measure occupational exhaustion. J Occup Environ Med, 49(8), 864–873.


OSA

occupational functioning and self-perception

Baron, K., Kielhofner, G., & colleagues (Model of Human Occupation framework)

The Occupational Self-Assessment (OSA) is a client-centered, reflective tool designed to measure an individual's perception of occupational functioning and identify areas of occupational concern or goals. Developed by Baron, Kielhofner, and colleagues within the Model of Human Occupation (MOHO) framework, the OSA integrates competence self-rating with importance rating, revealing the gap between what the client can do and what matters to them. The OSA is used in occupational therapy across mental health, physical rehabilitation, aging, and developmental disability to identify therapy goals and monitor changes in occupational functioning.

Self-report questionnaire and importance rating

Baron, K., Kielhofner, G., Iyenger, A., Goldhammer, V., & Wolenski, J. (2006). The Occupational Self Assessment (OSA) (2nd ed.). MOHO Clearinghouse, University of Illinois at Chicago.

Kielhofner, G., & Henry, A. D. (1988). Development and investigation of the occupational performance history interview. American Journal of Occupational Therapy, 42(8), 489-498.

copm occupational-self-assessment frenchay-activities-index upper-extremity-functional-scale


Occupational Stress Index

The Occupational Stress Index (OSI) is a comprehensive self-report measure of job-related stress and coping resources. Developed by Osipow and Spokane in 1987, the 140-item scale (abbreviated versions also exist) captures role overload, role boundary, role insufficiency, role ambiguity, physical environment demands, and coping resources. The OSI is grounded in stress and coping theory and predicts health outcomes, performance, and retention.

Osipow, S. H., & Spokane, A. R. (1987). Occupational Stress Inventory manual (Rev. ed.). Psychological Assessment Resources.

Spokane, A. R., Ohlund, B., Luchetta, E., & Meir, E. I. (1997). Validity of the Occupational Stress Inventory: Comparative and factorial validity with measures of job characteristics. Journal of Career Assessment, 5(3), 343–355.

Pearlman, D. S., Hartman, E. A., & O'Neill, C. B. (2000). Occupational stress: A review and critique of three major approaches. Journal of Organizational Behavior, 21(8), 915–933.

workplace-bullying-questionnaire perceived-organizational-support job-descriptive-index psychological-capital-questionnaire leader-member-exchange-scale


Ocean-Atmosphere Coupled Model

An Ocean-Atmosphere Coupled Model (AOGCM) is a comprehensive climate simulation that couples dynamic general circulation models of the atmosphere and ocean with explicit exchange of heat, momentum, and moisture at the interface. Developed by Manabe, Bryan, and colleagues in the 1970s, coupled models are essential for simulating climate change, ocean circulation changes, and climate-ocean interactions over decadal to centennial timescales.

Coupled atmosphere-ocean climate system simulation

Manabe, S., Bryan, K., & Spelman, M. J. (1975). A global ocean-atmosphere climate model with seasonal variation for future studies of climate sensitivity. Journal of Physical Oceanography, 5(1), 3-29.

Knutti, R., & Sedláček, J. (2013). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3(4), 369-373.


Ocean Color Chlorophyll-a

Ocean Color Chlorophyll-a Remote Sensing

Chlorophyll-a Retrieval
Ocean Productivity Monitoring

Ocean color remote sensing is the primary global method for retrieving seawater chlorophyll-a concentrations and phytoplankton productivity from satellite sensors. Based on bio-optical principles established in the 1970s, ocean color algorithms convert satellite spectral reflectance measurements into estimates of chlorophyll-a pigment concentration. This method enables global-scale, real-time monitoring of oceanic primary productivity and plankton dynamics.

Gordon, H. R., & Morel, A. Y. (1983). Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. Springer-Verlag.

Behrenfeld, M. J., & Falkowski, P. G. (2001). A consumer's guide to phytoplankton primary productivity models. Limnology and Oceanography, 46(7), 1639-1654.


Obsessive-Compulsive Inventory

The Obsessive-Compulsive Inventory-Revised (OCI-R) is an 18-item self-report measure of obsessive-compulsive disorder (OCD) symptoms. Developed by Foa and colleagues in 2002, the OCI-R is a revised and shortened version of the original OCI. It assesses six dimensions of OCD: obsessing, hoarding, neutralizing, contamination, responsibility, and symmetry. The OCI-R has become the standard self-report outcome measure in OCD research and treatment trials.

Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G., & Salkovskis, P. M. (2002). The Obsessive-Compulsive Inventory: Development and validation of a short version. Psychological Assessment, 14(4), 485-496.

Grisham, J. R., Fullana, M. A., Mataix-Cols, D., Moffitt, T. E., Caspi, A., & Foa, E. B. (2008). Risk factors prospectively associated with adult obsessive-compulsive symptoms: A comparison of two birth cohorts assessed at different life stages. American Journal of Psychiatry, 165(12), 1552-1560.


OCRA

OCRA (Operational Competitiveness Rating) is a ranking multi-criteria decision-making (MCDM) method introduced by Parkan, C. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Preference rating (input/output separation)

Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics