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

• DOI: 10.1016/j.csda.2015.08.005
• Publication Date: 2015-08-13T18:31:19Z
• Authors: Ryan Martin; Zhen Han
• Keywords: Methodology (stat.ME); FOS: Computer and information sciences; 0502 economics and business; 05 social sciences; 0101 mathematics; 16. Peace & justice; Statistics - Computation; 01 natural sciences; Statistics - Methodology; Computation (stat.CO); 3. Good health

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.<br/>17 pages, 4 figures, 2 tables<br/>