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A mean-constrained finite mixture of normals model

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  • Bao, Junshu
  • Hanson, Timothy E.

Abstract

A simple constructive approach to imposing a mean constraint in a finite mixture of multivariate Gaussian densities is proposed. All parameters in the model except for one have closed-form full conditional distributions and are fit through a simple Markov chain Monte Carlo algorithm. For illustration, the mean-constrained finite mixture is implemented in a linear mixed model. Simulations reveal that the mean-constrained model is able to precisely estimate the regression coefficients and mean-constrained random effects distribution simultaneously. An analysis of the Framingham cholesterol data shows that, with relatively simple structure, the model has competitive predictive power with earlier approaches.

Suggested Citation

  • Bao, Junshu & Hanson, Timothy E., 2016. "A mean-constrained finite mixture of normals model," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 93-99.
    • Handle: RePEc:eee:stapro:v:117:y:2016:i:c:p:93-99
    DOI: 10.1016/j.spl.2016.05.009
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    Linear mixed model; Stick-breaking prior;

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