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

Ryan Martin and Zhen Han

Computational Statistics & Data Analysis, 2016, vol. 94, issue C, 75-85

Abstract: To avoid specification of a particular distribution for the error in a regression model, we propose a flexible scale mixture model with a nonparametric mixing distribution. This model contains, among other things, the familiar normal and Student-t models as special cases. 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.

Keywords: EM algorithm; Dirichlet process; Marginal likelihood; Nonparametric maximum likelihood; Normal scale mixture; Profile likelihood (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:94:y:2016:i:c:p:75-85

DOI: 10.1016/j.csda.2015.08.005

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