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Misclassification Probabilities through Edgeworth-type Expansion for the Distribution of the Maximum Likelihood based Discriminant Function

This thesis covers misclassification probabilities via an Edgeworth-type expansion of the maximum likelihood based discriminant function. When deriving misclassification errors, first the expectation and variance in the population are assumed to be known where the variance is the same across populations and thereafter we consider the case where those parameters are unknown. Cumulants of the discriminant function for discriminating between two multivariate normal populations are derived. Approximate probabilities of the misclassification errors are established via an Edgeworth-type expansion using a standard normal distribution.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-175873
Date January 2021
CreatorsUmunoza Gasana, Emelyne
PublisherLinköpings universitet, Tillämpad matematik, Linköpings universitet, Tekniska fakulteten, Linköping, Sweden
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeLicentiate thesis, monograph, info:eu-repo/semantics/masterThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationLinköping Studies in Science and Technology. Licentiate Thesis, 0280-7971 ; 1911

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