Discriminant Analysis for Repeated Measures Data: Effects of Mean and Covariance Misspecification on Bias and Error in Discriminant Function Coefficients

Discriminant Analysis for Repeated Measures Data: Effects of Mean and Covariance Misspecification on Bias and Error in Discriminant Function Coefficients

Repeated measures discriminant analysis 1 Discriminant Analysis for Repeated Measures Data: Effects of Mean and Covariance Misspecification on Bias and Error in Discriminant Function Coefficients

Word Count: 47 Manuscript Word Count: 4387 Repeated measures discriminant analysis 2

Discriminant Analysis for Repeated Measures Data: A Review

Discriminant analysis (DA) encompasses procedures for classifying observations into groups (i.e., predictive discriminative analysis) and describing the relative importance of variables for distinguishing amongst groups (i.e., descriptive discriminative analysis). In recent years, a number of developments have occurred in DA procedures for the analysis of data from repeated measures designs. Sp...

Robust descriptive discriminant analysis for repeated measures data

Robust repeated measures discriminant analysis (RMDA) procedures based on parsimonious covariance structures were developed using trimmed estimators. The e ects of non-normality, covariance structure, and mean con guration on bias and root mean square error (RMSE) of RMDA coe cients were studied using Monte Carlo techniques. The bias and RMSE values of robust RMDA coe cients were at least 10% a...

Covariance-enhanced discriminant analysis.

Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporati...