Journal article Open Access

Sex estimation: A comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis and neural networks using ordinal variables

Efthymia Nikita; Panos Nikitas

The performance of six classification methods, binary logistic (BLR), probit (PR) and cumulative probit (CPR) regression, linear (LDA) and quadratic (QDA) discriminant analysis, and artificial neural networks (ANN), is examined in skeletal sex estimation. These methods were tested using cranial and pelvic sexually dimorphic traits recorded on a modern documented collection, the Athens Collection. For their implementation, an R package has been written to perform cross-validated (CV) sex classification and give the discriminant function of each of the methods studied. A simple algorithm that combines two discriminant functions is also proposed. It was found that the differences in the classification performance between BLR, PR, CPR, LDA, QDA, and ANN are overall small. However, LDA is simpler and more flexible than CPR, QDA and ANN and has a small but clear advantage over BLR and PR. Consequently, LDA may be preferred in skeletal sex estimation. Finally, it is striking that the combination of pelvic and cranial traits via their discriminant functions determined either by BLR or LDA removes practically any population-specificity and yields much better predictions than the individual functions; in fact, the prediction accuracy increases above 97%.

The research for this project was also supported by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation (People in Motion project: EXCELLENCE/1216/0023).
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