29 Citations (Scopus)

Abstract

A mixture of the MANOVA and GMANOVA models is presented. The expected value of the response matrix in this model is the sum of two matrix components. The first component represents the GMANOVA portion and the second component represents the MANOVA portion. Maximum likelihood estimators are derived for the parameters in this model, and goodness-of-fit tests are constructed for fuller models via the likelihood ratio criterion. Finally, likelihood ratio tests for general linear hypotheses are developed and a numerical example is presented.

Original languageEnglish (US)
Pages (from-to)3075-3089
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume14
Issue number12
DOIs
StatePublished - Jan 1 1985

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Multivariate Analysis of Variance
Likelihood Ratio Criterion
Linear Hypothesis
Goodness of Fit Test
Likelihood Ratio Test
Expected Value
Model
Maximum Likelihood Estimator
Numerical Examples

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Cite this

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abstract = "A mixture of the MANOVA and GMANOVA models is presented. The expected value of the response matrix in this model is the sum of two matrix components. The first component represents the GMANOVA portion and the second component represents the MANOVA portion. Maximum likelihood estimators are derived for the parameters in this model, and goodness-of-fit tests are constructed for fuller models via the likelihood ratio criterion. Finally, likelihood ratio tests for general linear hypotheses are developed and a numerical example is presented.",
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year = "1985",
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A mixture of the MANOVA and GMANOVA models. / Chinchilli, Vernon; Elswick, Ronald K.

In: Communications in Statistics - Theory and Methods, Vol. 14, No. 12, 01.01.1985, p. 3075-3089.

Research output: Contribution to journalArticle

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AU - Chinchilli, Vernon

AU - Elswick, Ronald K.

PY - 1985/1/1

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AB - A mixture of the MANOVA and GMANOVA models is presented. The expected value of the response matrix in this model is the sum of two matrix components. The first component represents the GMANOVA portion and the second component represents the MANOVA portion. Maximum likelihood estimators are derived for the parameters in this model, and goodness-of-fit tests are constructed for fuller models via the likelihood ratio criterion. Finally, likelihood ratio tests for general linear hypotheses are developed and a numerical example is presented.

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