Efficient posterior sampling for high-dimensional imbalanced logistic regression.

Journal Article (Journal Article)

Classification with high-dimensional data is of widespread interest and often involves dealing with imbalanced data. Bayesian classification approaches are hampered by the fact that current Markov chain Monte Carlo algorithms for posterior computation become inefficient as the number [Formula: see text] of predictors or the number [Formula: see text] of subjects to classify gets large, because of the increasing computational time per step and worsening mixing rates. One strategy is to employ a gradient-based sampler to improve mixing while using data subsamples to reduce the per-step computational complexity. However, the usual subsampling breaks down when applied to imbalanced data. Instead, we generalize piecewise-deterministic Markov chain Monte Carlo algorithms to include importance-weighted and mini-batch subsampling. These maintain the correct stationary distribution with arbitrarily small subsamples and substantially outperform current competitors. We provide theoretical support for the proposed approach and demonstrate its performance gains in simulated data examples and an application to cancer data.

Full Text

Duke Authors

Cited Authors

  • Sen, D; Sachs, M; Lu, J; Dunson, DB

Published Date

  • December 2020

Published In

Volume / Issue

  • 107 / 4

Start / End Page

  • 1005 - 1012

PubMed ID

  • 33462537

Pubmed Central ID

  • PMC7799181

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asaa035


  • eng