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Selection of terms in random coefficient regression models

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  • Francisco M. M. Rocha
  • Julio M. Singer

Abstract

The selection of suitable terms in random coefficient regression models is a challenging problem to practitioners. Although many techniques, ranging from those with a theoretical flavour to those with an exploratory spirit, have been proposed for such purposes, no particular one may be considered as a paradigm. In fact, many authors advocate that they should be used in a complementary way. We consider exploratory methods based on fitting standard regression models to the individual response profiles or to the rows of the sample within-units covariance matrix (for balanced data) that may serve as additional tools in the process of selecting an appropriate model. We evaluate the performance of the proposal via a simulation study and consider applications to two examples in the field of Biostatistics.

Suggested Citation

  • Francisco M. M. Rocha & Julio M. Singer, 2018. "Selection of terms in random coefficient regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(2), pages 225-242, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:225-242
    DOI: 10.1080/02664763.2016.1273884
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    References listed on IDEAS

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    1. Orelien, Jean G. & Edwards, Lloyd J., 2008. "Fixed-effect variable selection in linear mixed models using R2 statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1896-1907, January.
    2. Pu, Wenji & Niu, Xu-Feng, 2006. "Selecting mixed-effects models based on a generalized information criterion," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 733-758, March.
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    Cited by:

    1. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.

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