A note on maximum likelihood estimation for covariance reducing models

    Authors

    J. R. Schott

    Comments

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    Abbreviated Journal Title

    Stat. Probab. Lett.

    Keywords

    Eigenanalysis; Wishart distribution; Statistics & Probability

    Abstract

    Cook and Forzani (2008) proposed covariance reducing models as a method for modeling the differences among k covariance matrices. The model was developed via a property of a conditional distribution for the sample covariance matrices and this conditional distribution was used to obtain maximum likelihood estimators. In this work, we show that the same maximum likelihood estimators can be obtained using the unconditional distribution of the sample covariance matrices along with a condition on the population covariance matrices that holds if and only if the covariance reducing model holds. In addition, it is shown that when k = 2, specialized numerical methods are not needed to compute the maximum likelihood estimators. (C) 2012 Elsevier B.V. All rights reserved.

    Journal Title

    Statistics & Probability Letters

    Volume

    82

    Issue/Number

    9

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    1629

    Last Page

    1631

    WOS Identifier

    WOS:000306775500001

    ISSN

    0167-7152

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