Asymptotic distribution for the proportional covariance model

03/21/2021

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by   Myung Geun Kim, et al.

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Asymptotic distribution for the proportional covariance model under multivariate normal distributions is derived. To this end, the parametrization of the common covariance matrix by its Cholesky root is adopted. The derivations are made in three steps. First, the asymptotic distribution of the maximum likelihood estimators of the proportionality coefficients and the Cholesky inverse root of the common covariance matrix is derived by finding the information matrix and its inverse. Next, the asymptotic distributions for the case of the Cholesky root of the common covariance matrix and finally for the case of the common covariance matrix itself are derived using the multivariate δ-method. As an application of the asymptotic distribution derived here, a hypothesis for homogeneity of covariance matrices is considered.