Contributions to Discriminant Analysis of Cross-sectional and Longitudinal Data with Applications
Author: Alice M. Hinton
Publisher:
Published: 2014
Total Pages:
ISBN-13:
DOWNLOAD EBOOKThere are a variety of methods available to classify an object into one of two populations. Here, the method of discriminant analysis is considered in the cross-sectional and the longitudinal setting with a structured multivariate normal model. The generalized likelihood ratio change detection algorithm is also investigated as an alternative to methods based on discriminant analysis in the longitudinal setting. Traditionally, discriminant functions are developed to classify a new observation from a cross-sectional dataset into a population. An error is made when the observation is incorrectly classified. In the literature, several parametric and empirical methods of estimating these misclassification probabilities have been proposed. The performance of six parametric and three empirical misclassification probability estimators are compared. It is found that the parametric methods, which rely on an assumption of normality, generally outperform the empirical methods when a linear discriminant function is used for classification and the data originate from normal populations. The preferred parametric method depends on the size of the training dataset and the parameters of the populations, particularly the distance between the means. The empirical methods are preferred only when the two populations are well separated and the variances are significantly different.
