In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p

J E Griffin, K G Łatuszyński and Mark Steel

Biometrika, 2021, vol. 108, issue 1, 53-69

Abstract: SummaryThe availability of datasets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these datasets has proved difficult since available Markov chain Monte Carlo methods do not perform well in typical problem sizes of interest. We propose new adaptive Markov chain Monte Carlo algorithms to address this shortcoming. The adaptive design of these algorithms exploits the observation that in large-$p$, small-$n$ settings, the majority of the $p$ variables will be approximately uncorrelated a posteriori. The algorithms adaptively build suitable nonlocal proposals that result in moves with squared jumping distance significantly larger than standard methods. Their performance is studied empirically in high-dimensional problems and speed-ups of up to four orders of magnitude are observed.

Keywords: Expected squared jumping distance; High-dimensional data; Large-p; small-n problem; Linear regression; Optimal scaling; Spike-and-slab prior; Variable selection (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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