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

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

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...

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...

Online Monitoring and Fault Diagnosis of Multivariate-attribute Process Mean Using Neural Networks and Discriminant Analysis Technique

In some statistical process control applications, the process data are not Normally distributed and characterized by the combination of both variable and attributes quality characteristics. Despite different methods which are proposed separately for monitoring multivariate and multi-attribute processes, only few methods are available in the literature for monitoring multivariate-attribute proce...

Non-asymptotic Analysis of Compressive Fisher Discriminants in terms of the Effective Dimension

We provide a non-asymptotic analysis of the generalisation error of compressive Fisher linear discriminant (FLD) classification that is dimension free under mild assumptions. Our analysis includes the effects that random projection has on classification performance under covariance model misspecification, as well as various good and bad effects of random projections that contribute to the overa...