A mean-constrained finite mixture of normals model

Junshu Bao and Timothy E. Hanson

Statistics & Probability Letters, 2016, vol. 117, issue C, 93-99

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.

Keywords: Linear mixed model; Stick-breaking prior (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1016/j.spl.2016.05.009

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