Article ID Journal Published Year Pages File Type
1152227 Statistics & Probability Letters 2012 6 Pages PDF
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.
Related Topics
Physical Sciences and Engineering Mathematics Statistics and Probability
Authors
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