DMCA
Parameter Expansion for Data Augmentation (1999)
| Venue: | Journal of the American Statistical Association |
| Citations: | 114 - 3 self |
BibTeX
@ARTICLE{Liu99parameterexpansion,
author = {Jun S. Liu and Ying Nian Wu},
title = {Parameter Expansion for Data Augmentation},
journal = {Journal of the American Statistical Association},
year = {1999},
volume = {94},
pages = {1264--1274}
}
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Abstract
Viewing the observed data of a statistical model as incomplete and augmenting its missing parts are useful for clarifying concepts and central to the invention of two well-known statistical algorithms: expectation-maximization (EM) and data augmentation. Recently, Liu, Rubin, and Wu (1998) demonstrate that expanding the parameter space along with augmenting the missing data is useful for accelerating iterative computation in an EM algorithm. The main purpose of this article is to rigorously define a parameter expanded data augmentation (PX-DA) algorithm and to study its theoretical properties. The PX-DA is a special way of using auxiliary variables to accelerate Gibbs sampling algorithms and is closely related to reparameterization techniques. Theoretical results concerning the convergence rate of the PX-DA algorithm and the choice of prior for the expansion parameter are obtained. In order to understand the role of the expansion parameter, we establish a new theory for iterative condi...
Keyphrases
data augmentation parameter expansion expansion parameter parameter space well-known statistical algorithm new theory main purpose special way convergence rate observed data auxiliary variable statistical model px-da algorithm theoretical result iterative condi theoretical property em algorithm reparameterization technique missing part iterative computation
