Parameter Expansion for Data Augmentation

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 demonstrated 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. We obtain theoretical results concerning the convergence rate of the PX-DA algorithm and the choice of prior for the expansion parameter. To understand the role of the expansion parameter, we establish a new theory for iterative conditional sampling under the transformation group formulation, which generalizes the standard Gibbs sampler. Using the new theory, we show that the PX-DA algorithm with a Haar measure prior (often improper) for the expansion parameter is always proper and is optimal among a class of such algorithms including reparameterization.