A semiparametric scale-mixture regression model and predictive recursion maximum likelihood

Ryan Martin; Zhen Han

doi:10.1016/j.csda.2015.08.005

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arXiv · 1306.3185

A semiparametric scale-mixture regression model and predictive recursion maximum likelihood

Ryan Martin·Zhen Han

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Abstract

To avoid specification of the error distribution in a regression model, we propose a general nonparametric scale mixture model for the error distribution. For fitting such mixtures, the predictive recursion method is a simple and computationally efficient alternative to existing methods. We define a predictive recursion-based marginal likelihood function, and estimation of the regression parameters proceeds by maximizing this function. A hybrid predictive recursion--EM algorithm is proposed for this purpose. The method's performance is compared with that of existing methods in simulations and real data analyses.

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Ryan Martin, Zhen Han. 2013-06-13. A semiparametric scale-mixture regression model and predictive recursion maximum likelihood. https://doi.org/10.1016/j.csda.2015.08.005

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