Sparse variational analysis of linear mixed models for large data sets

Published

Journal Article

It is increasingly common to be faced with longitudinal or multi-level data sets that have large numbers of predictors and/or a large sample size. Current methods of fitting and inference for mixed effects models tend to perform poorly in such settings. When there are many variables, it is appealing to allow uncertainty in subset selection and to obtain a sparse characterization of the data. Bayesian methods are available to address these goals using Markov chain Monte Carlo (MCMC), but MCMC is very computationally expensive and can be infeasible in large p and/or large n problems. As a fast approximate Bayes solution, we recommend a novel approximation to the posterior relying on variational methods. Variational methods are used to approximate the posterior of the parameters in a decomposition of the variance components, with priors chosen to obtain a sparse solution that allows selection of random effects. The method is evaluated through a simulation study, and applied to an epidemiological application. © 2011 Elsevier B.V.

Full Text

Duke Authors

Cited Authors

  • Armagan, A; Dunson, D

Published Date

  • August 1, 2011

Published In

Volume / Issue

  • 81 / 8

Start / End Page

  • 1056 - 1062

International Standard Serial Number (ISSN)

  • 0167-7152

Digital Object Identifier (DOI)

  • 10.1016/j.spl.2011.02.029

Citation Source

  • Scopus