 A theory for the multiset samplerYuguo Chen Statistics & Probability Letters, 2012, vol. 82, issue 3, 473-477 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. Keywords: Data augmentation; Evolutionary forest algorithm; Forward operator; Gibbs sampler; Markov chain Monte Carlo (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:82:y:2012:i:3:p:473-477 Ordering information: This journal article can be ordered from DOI: 10.1016/j.spl.2011.09.025 Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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