Approximation of misclassification probabilities in linear discriminant analysis based on repeated measurements

• Published in: Communications in statistics. Theory and methods Vol. 52; no. 23; pp. 8388 - 8407
• Main Authors: Ngailo, Edward Kanuti, Chuma, Furaha
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.12.2023; Taylor & Francis Ltd
• Subjects: Approximation; Covariance matrix; Discriminant analysis; Environmental studies; extended growth curve model; growth curve model; linear discriminant function; Mathematical analysis; Monte Carlo simulation; probability of misclassification
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610926.2022.2062605

The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this article repeated measurements follow an extended growth curve model and are classified using linear discriminant analysis. The aim of this article is to propose approximation for the misclassification probabilities in the linear discriminant function when the population means follow an extended growth curve structure. Using specific statistic relations we derive the approximation of misclassification probabilities for known and unknown covariance matrices. Finally, we perform a Monte Carlo simulation study to assess the accuracy of the developed results.