An Edgeworth-type expansion for the distribution of a likelihood-based discriminant function

Abstract

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

Keywords

Classification rule; discriminant analysis; Edgeworth-type expansion; missclassification errors

Published in

Journal of Statistical Computation and Simulation
2023, volume: 93, number: 17, pages: 3185-3202
Publisher: TAYLOR AND FRANCIS LTD

SLU Authors

• von Rosen, Dietrich

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

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1080/00949655.2023.2219358

Permanent link to this page (URI)

https://res.slu.se/id/publ/122695