A novel reversible jump algorithm for generalized linear models

M. Papathomas, Petros Dellaportas and V. G. S. Vasdekis

Biometrika, 2011, vol. 98, issue 1, 231-236

Abstract: We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities, together with the resulting freedom to propose moves between any models, improve the performance of the reversible jump algorithm. Copyright 2011, Oxford University Press.

Date: 2011
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