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Spectral analytic comparisons for data augmentation

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  • Roy, Vivekananda

Abstract

The sandwich algorithm (SA) is an alternative to the data augmentation (DA) algorithm that uses an extra simulation step at each iteration. In this paper, we show that the sandwich algorithm always converges at least as fast as the DA algorithm, in the Markov operator norm sense. We also establish conditions under which the spectrum of SA dominates that of DA. An example illustrates the results.

Suggested Citation

  • Roy, Vivekananda, 2012. "Spectral analytic comparisons for data augmentation," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 103-108.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:1:p:103-108
    DOI: 10.1016/j.spl.2011.09.009
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    References listed on IDEAS

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    1. Rosenthal J.S., 2003. "Asymptotic Variance and Convergence Rates of Nearly-Periodic Markov Chain Monte Carlo Algorithms," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 169-177, January.
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    Cited by:

    1. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.

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