Data augmentation for non-Gaussian regression models using variance-mean mixtures

N. G. Polson and J. G. Scott

Biometrika, 2013, vol. 100, issue 2, 459-471

Abstract: We use the theory of normal variance-mean mixtures to derive a data-augmentation scheme for a class of common regularization problems. This generalizes existing theory on normal variance mixtures for priors in regression and classification. It also allows variants of the expectation-maximization algorithm to be brought to bear on a wider range of models than previously appreciated. We demonstrate the method on several examples, focusing on the case of binary logistic regression. We also show that quasi-Newton acceleration can substantially improve the speed of the algorithm without compromising its robustness. Copyright 2013, Oxford University Press.

Date: 2013
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