JSM 2004 - Toronto

Abstract #301700

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Activity Number: 440
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301700
Title: An Update on the NIST Statistical Reference Datasets for MCMC
Author(s): Hung-kung Liu*+ and William F. Guthrie and Donald Malec and Grace Yang
Companies: National Institute of Standards and Technology and National Institute of Standards and Technology and National Institute of Standards and Technology and National Institute of Standards and Technology
Address: Statistical Engineering Division, Gaithersburg, MD, 20899,
Keywords: Statistical Reference Datasets (StRD) ; Markov chain Monte Carlo (MCMC) ; numerical accuracy ; floating point arithmetic ; Bayesian analysis
Abstract:

In the Statistical Reference Datasets project, NIST provided datasets on the web (www.itl.nist.gov/div898/strd/index.html) with certified values for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. A new area in statistical computing is the Bayesian analysis using Markov chain Monte Carlo. Despite of its importance, the numerical accuracy of the software for MCMC is largely unknown. We have recently updated the StRD web site with the six new datasets for Bayesian model fitting using MCMC algorithms. We will discuss some results obtained using these datasets that challenge the conventional wisdom that longer simulations lead to improved approximation of the posterior distribution.

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