On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis

Amin Zollanvari, Marc G. Genton

Research output: Contribution to journalArticle

Abstract

We provide a fundamental theorem that can be used in conjunction with Kolmogorov asymptotic conditions to derive the first moments of well-known estimators of the actual error rate in linear discriminant analysis of a multivariate Gaussian model under the assumption of a common known covariance matrix. The estimators studied in this paper are plug-in and smoothed resubstitution error estimators, both of which have not been studied before under Kolmogorov asymptotic conditions. As a result of this work, we present an optimal smoothing parameter that makes the smoothed resubstitution an unbiased estimator of the true error. For the sake of completeness, we further show how to utilize the presented fundamental theorem to achieve several previously reported results, namely the first moment of the resubstitution estimator and the actual error rate. We provide numerical examples to show the accuracy of the succeeding finite sample approximations in situations where the number of dimensions is comparable or even larger than the sample size.

Original languageEnglish
Pages (from-to)300-326
Number of pages27
JournalSankhya A
Volume75
Issue number2
DOIs
Publication statusPublished - Aug 1 2013

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Misclassification Error
Discriminant Analysis
Error Rate
Estimator
Moment
Smoothing Parameter
Unbiased estimator
Error Estimator
Multivariate Models
Gaussian Model
Plug-in
Optimal Parameter
Theorem
Covariance matrix
Completeness
Sample Size
Numerical Examples
Approximation
Discriminant analysis
Misclassification error

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

On Kolmogorov asymptotics of estimators of the misclassification error rate in linear discriminant analysis. / Zollanvari, Amin; Genton, Marc G.

In: Sankhya A, Vol. 75, No. 2, 01.08.2013, p. 300-326.

Research output: Contribution to journalArticle

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