Open AccessAsymptotic approximation of misclassification probabilities in linear discriminant analysis with repeated measurements
Author(s) -
Edward Ngailo,
Dietrich von Rosen,
Martin Singull
Publication year - 2021
Publication title -
acta et commentationes universitatis tartuensis de mathematica./acta et commentationes universitatis tartuensis de mathematica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.276
H-Index - 6
eISSN - 2228-4699
pISSN - 1406-2283
DOI - 10.12697/acutm.2021.25.05
Subject(s) - linear discriminant analysis , mathematics , covariance matrix , discriminant , monte carlo method , covariance , statistics , optimal discriminant analysis , multivariate statistics , discriminant function analysis , asymptotic analysis , multivariate normal distribution , artificial intelligence , computer science
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.