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A test of separate hypotheses for comparing linear mixed models with non nested fixed effects

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  • Ché L. Smith
  • Lloyd J. Edwards

Abstract

As researchers increasingly rely on linear mixed models to characterize longitudinal data, there is a need for improved techniques for selecting among this class of models which requires specification of both fixed and random effects via a mean model and variance-covariance structure. The process is further complicated when fixed and/or random effects are non nested between models. This paper explores the development of a hypothesis test to compare non nested linear mixed models based on extensions of the work begun by Sir David Cox. We assess the robustness of this approach for comparing models containing correlated measures of body fat for predicting longitudinal cardiometabolic risk.

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  • Ché L. Smith & Lloyd J. Edwards, 2017. "A test of separate hypotheses for comparing linear mixed models with non nested fixed effects," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(11), pages 5487-5500, June.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:11:p:5487-5500
    DOI: 10.1080/03610926.2015.1104352
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