An alternative REML estimation of covariance matrices in linear mixed models Academic Article

abstract

• We propose a data-driven procedure for modeling covariance matrices in linear mixed-effects models with minimal distributional assumption on the random effects. It is based on elimination of the random effects using a transformation of the response variable. The approach makes it possible for the first time to disentangle the covariance matrices and model them separately. The performance of the proposed method is assessed via simulations and real data. 2012 Elsevier B.V..

authors

• Pourahmadi, Mohsen

published proceedings

• STATISTICS & PROBABILITY LETTERS

altmetric score

• 0.25

author list (cited authors)

• Li, E., & Pourahmadi, M.

citation count

• 1

complete list of authors

• Li, Erning||Pourahmadi, Mohsen

publication date

• January 2013

publisher

• Elsevier  Publisher

published in

• Statistics and Probability Letters  Journal

keywords

• Cholesky Decomposition
• Covariance Matrices
• Longitudinal Data
• Mixed Models
• Restricted Or Residual Maximum Likelihood (reml)

Digital Object Identifier (DOI)

• 10.1016/j.spl.2012.12.028

start page

• 1071

end page

• 1077

volume

• 83

issue

• 4

URL

• http://dx.doi.org/10.1016/j.spl.2012.12.028