Spectral analytic comparisons for data augmentation

Vivekananda Roy

Statistics & Probability Letters, 2012, vol. 82, issue 1, 103-108

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.

Keywords: Compact operator; Convergence rate; Data augmentation algorithm; Eigenvalue; Markov chain (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1016/j.spl.2011.09.009

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