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November 2010 Cross-Fertilizing Strategies for Better EM Mountain Climbing and DA Field Exploration: A Graphical Guide Book
David A. van Dyk, Xiao-Li Meng
Statist. Sci. 25(4): 429-449 (November 2010). DOI: 10.1214/09-STS309


In recent years, a variety of extensions and refinements have been developed for data augmentation based model fitting routines. These developments aim to extend the application, improve the speed and/or simplify the implementation of data augmentation methods, such as the deterministic EM algorithm for mode finding and stochastic Gibbs sampler and other auxiliary-variable based methods for posterior sampling. In this overview article we graphically illustrate and compare a number of these extensions, all of which aim to maintain the simplicity and computation stability of their predecessors. We particularly emphasize the usefulness of identifying similarities between the deterministic and stochastic counterparts as we seek more efficient computational strategies. We also demonstrate the applicability of data augmentation methods for handling complex models with highly hierarchical structure, using a high-energy high-resolution spectral imaging model for data from satellite telescopes, such as the Chandra X-ray Observatory.


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David A. van Dyk. Xiao-Li Meng. "Cross-Fertilizing Strategies for Better EM Mountain Climbing and DA Field Exploration: A Graphical Guide Book." Statist. Sci. 25 (4) 429 - 449, November 2010.


Published: November 2010
First available in Project Euclid: 14 March 2011

zbMATH: 1329.62040
MathSciNet: MR2807762
Digital Object Identifier: 10.1214/09-STS309

Keywords: AECM , blocking , collapsing , conditional augmentation , Data augmentation , ECM , ECME , efficient augmentation , Gibbs sampling , marginal augmentation , model reduction , NEM , nesting

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.25 • No. 4 • November 2010
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