JSM Activity #140

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Activity ID:  140
Title
Data Augmentation Methods
Date / Time / Room Sponsor Type
08/12/2002
2:00 PM - 3:50 PM
Room: H-Morgan Suite 		Section on Statistical Computing* Topic Contributed
Organizer: David A. van Dyk, Harvard University
Chair: Xiao-Li Meng, Harvard University
Discussant:  
Floor Discussion 3:45 PM
Description

Highly-structured application-specific statistical models that aim to account for complexity in both the underlying substantive process and the data collection mechanism (e.g., missing data) are increasingly applied in the physical, biological, social, and engineering sciences. Data augmentation offers a coherent and fruitful strategy for overcoming computational challenges for many such complex models. We explore a number of recent advances in DA methods, including stochastic samplers and deterministic mode finders, and creative implementations to obtain fast convergence.
  300520  By:  Richard A. Levine 2:05 PM 08/12/2002
An Automated (Markov Chain) Monte Carlo EM Algorithm

  301109  By:  David A. van Dyk 2:25 PM 08/12/2002
Incompatibility in Gibbs Samplers

  300546  By:  Kosuke  Imai 2:45 PM 08/12/2002
A Bayesian Analysis of the Multinomial Probit Model Using Marginal Data Augmentation

  301663  By:  Thomas C. M. Lee 3:05 PM 08/12/2002
Self-Consistency and Wavelet Regressions with Irregular Designs

  301112  By:  Recai M. Yucel 3:25 PM 08/12/2002
Data Augmentation Strategies in Multivariate Multilevel Incomplete Data

JSM 2002

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Revised March 2002