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A theory for the multiset sampler

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  • Chen, Yuguo
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    Abstract

    The multiset sampler (MSS) can be viewed as a new data augmentation scheme and it has been applied successfully to a wide range of statistical inference problems. The key idea of the MSS is to augment the system with a multiset of the missing components, and construct an appropriate joint distribution of the parameters of interest and the missing components to facilitate the inference based on Markov chain Monte Carlo. The standard data augmentation strategy corresponds to the MSS with multiset size one. This paper provides a theoretical comparison of the MSS with different multiset sizes. We show that the MSS converges to the target distribution faster as the multiset size increases. This explains the improvement in convergence rate for the MSS with large multiset sizes over the standard data augmentation scheme.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 82 (2012)
    Issue (Month): 3 ()
    Pages: 473-477

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    Handle: RePEc:eee:stapro:v:82:y:2012:i:3:p:473-477

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    Related research

    Keywords: Data augmentation; Evolutionary forest algorithm; Forward operator; Gibbs sampler; Markov chain Monte Carlo;

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    1. Leman, Scotland C. & Chen, Yuguo & Lavine, Michael, 2009. "The Multiset Sampler," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1029-1041.
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