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

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  • Edward Kanuti Ngailo
  • Furaha Chuma

Abstract

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.

Suggested Citation

  • Edward Kanuti Ngailo & Furaha Chuma, 2023. "Approximation of misclassification probabilities in linear discriminant analysis based on repeated measurements," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(23), pages 8388-8407, December.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:23:p:8388-8407
    DOI: 10.1080/03610926.2022.2062605
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