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Batch Size Selection for Variance Estimators in MCMC

Author

Listed:
  • Ying Liu

    (University of California)

  • Dootika Vats

    (Indian Institute of Technology Kanpur)

  • James M. Flegal

    (University of California)

Abstract

We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for high-dimensional Markov chain Monte Carlo simulations. We derive the asymptotic mean squared error for this class of estimators. Further, we propose a parametric technique for estimating optimal batch sizes and discuss practical issues regarding the estimating process. Vector auto-regressive, Bayesian logistic regression, and Bayesian dynamic space-time examples illustrate the quality of the estimation procedure where the proposed optimal batch sizes outperform current batch size selection methods.

Suggested Citation

  • Ying Liu & Dootika Vats & James M. Flegal, 2022. "Batch Size Selection for Variance Estimators in MCMC," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 65-93, March.
    • Handle: RePEc:spr:metcap:v:24:y:2022:i:1:d:10.1007_s11009-020-09841-7
    DOI: 10.1007/s11009-020-09841-7
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    References listed on IDEAS

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