The likelihood ratio test for equality of mean vectors with compound symmetric covariance matrices

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The author derives the likelihood ratio test statistic for the equality of mean vectors when the covariance matrices are assumed to have a compound symmetric structure. Its exact distribution is then expressed in terms of a product of independent Beta random variables and it is shown that for some particular cases it is possible to obtain very manageable finite form expressions for the probability density and cumulative distribution functions for this distribution. For the other cases, given the intractability of the expressions for the exact distribution, very sharp near-exact distributions are developed. Numerical studies show the extreme good performance of these near-exact distributions.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2017 - 17th International Conference, 2017
PublisherSpringer Verlag
Pages20-32
Number of pages13
Volume10408 LNCS
ISBN (Electronic)978-3-319-62404-4
ISBN (Print)978-3-319-62403-7
DOIs
Publication statusPublished - 1 Jan 2017
Event17th International Conference on Computational Science and Its Applications, ICCSA 2017 - Trieste, Italy
Duration: 3 Jul 20176 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10408 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Computational Science and Its Applications, ICCSA 2017
CountryItaly
CityTrieste
Period3/07/176/07/17

Keywords

  • Beta distributions
  • Exact distribution
  • Likelihood ratio statistic
  • Near-exact distributions

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