Covariance components selection in high-dimensional growth curve model with random coefficients

Shinpei Imori and Dietrich von Rosen

Journal of Multivariate Analysis, 2015, vol. 136, issue C, 86-94

Abstract: In this paper, the true number of covariance components in a high-dimensional growth curve model with random coefficients are selected. We propose a selection criterion based on a concept from information theory. The proposed criterion satisfies a consistency property of the true covariance components in our high-dimensional setting. The performance of the proposed methodology is illustrated in a simulation study.

Keywords: Covariance components analysis; Generalized information criterion; Growth curve model with random coefficients (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.jmva.2015.01.010

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