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

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. (C) 2015 Elsevier Inc. All rights reserved.

Keywords

Covariance components analysis; Generalized information criterion; Growth curve model with random coefficients

Published in

Journal of Multivariate Analysis
2015, volume: 136, pages: 86-94

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    
  • Linköping University

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1016/j.jmva.2015.01.010

Permanent link to this page (URI)

https://res.slu.se/id/publ/73451