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

Sammanfattning

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.

Nyckelord

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

Publicerad i

Journal of Statistical Computation and Simulation
2023, volym: 93, nummer: 17, sidor: 3185-3202
Utgivare: TAYLOR AND FRANCIS LTD

SLU författare

• von Rosen, Dietrich

  • Institutionen för energi och teknik, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Sannolikhetsteori och statistik

Publikationens identifierare

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

Permanent länk till denna sida (URI)

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