A fast regression via SVD and marginalization

Philip Greengard (), Andrew Gelman and Aki Vehtari
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Philip Greengard: Columbia University
Andrew Gelman: Columbia University
Aki Vehtari: Aalto University

Computational Statistics, 2022, vol. 37, issue 2, No 7, 720 pages

Abstract: Abstract We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of the evaluation is a change of basis from coefficient space to the space of singular vectors of the matrix of predictors. After this change of basis and an analytical integration, we reduce the problem of finding moments of a density over $$k + 2$$ k + 2 dimensions, to finding moments of a 2-dimensional density, where k is the number of coefficients. Moments can then be computed using, for example, MCMC, the trapezoid rule, or adaptive Gaussian quadrature. An evaluation of the SVD of the matrix of predictors is the dominant computational cost and is performed once during the precomputation stage. We demonstrate numerical results of the algorithm.

Keywords: Bayesian Regression; Singular Value Decomposition; Marginalization; Fast Algorithms (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01135-x

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