Asymptotic approximation of misclassification probabilities in linear discriminant analysis with repeated measurements

Abstract

We propose asymptotic approximations for the probabilities of misclassification in linear discriminant analysis when the group means follow a growth curve structure. The discriminant function can classify a new observation vector of p repeated measurements into one of several multivariate normal populations with equal covariance matrix. We derive certain relations of the statistics under consideration in order to obtain asymptotic approximation of misclassification errors for the two group case. Finally, we perform Monte Carlo simulations to evaluate the reliability of the proposed results.

Keywords

Asymptotic approximation; Growth Curve model; linear discriminant function; probability of misclassification

Published in

Acta et Commentationes Universitatis Tartuensis de Mathematica
2021, Volume: 25, number: 1, pages: 67-85
Publisher: UNIV TARTU PRESS

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    
  • Linköping University

UKÄ Subject classification

Probability Theory and Statistics


Publication identifier

DOI: https://doi.org/10.12697/ACUTM.2021.25.05

Permanent link to this page (URI)

https://res.slu.se/id/publ/123255