A0154
Title: Efficient data augmentation techniques for some classes of state space models
Authors: Siew Li Linda Tan - National University of Singapore (Singapore) [presenting]
Abstract: Data augmentation improves the convergence of iterative algorithms, such as the EM algorithm and Gibbs sampler, by introducing carefully designed latent variables. We first propose a data augmentation scheme for the first-order autoregression plus noise model, where optimal values of working parameters introduced for recentering and rescaling of the latent states, can be derived analytically by minimizing the fraction of missing information in the EM algorithm. The proposed data augmentation scheme is then utilized to design efficient Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of some non-Gaussian and nonlinear state space models, via a mixture of normals approximation coupled with a block-specific reparametrization strategy. Applications on simulated and benchmark real datasets indicate that the proposed MCMC sampler can yield improvements in simulation efficiency compared with centering, noncentering and even the ancillarity-sufficiency interweaving strategy.
