Improved testing inference in mixed linear models

  • Authors:

  • Tatiane F. N. Melo;Silvia L. P. Ferrari;Francisco Cribari-Neto

  • Affiliations:

  • Departamento de Estatística, Universidade de São Paulo, Rua do Matão, 1010, São Paulo/SP, 05508-090, Brazil;Departamento de Estatística, Universidade de São Paulo, Rua do Matão, 1010, São Paulo/SP, 05508-090, Brazil;Departamento de Estatística, Universidade Federal de Pernambuco, Cidade Universitária, Recife/PE, 50740-540, Brazil

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2009

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Abstract

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62, 827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed.