Inference from Iterative Simulation Using Multiple Sequences
View/ Open
Rubin_InferenceIterSim.pdf (2.605Mb)
Access Status
Full text of the requested work is not available in DASH at this time ("restricted access"). For more information on restricted deposits, see our FAQ.Published Version
https://doi.org/10.1214/ss%2F1177011136Metadata
Show full item recordCitation
Gelman, Andrew, and Donald B. Rubin. 1992. Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 7(4): 457-472.Abstract
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.Other Sources
http://www.stat.duke.edu/~scs/Courses/Stat376/Papers/ConvergeDiagnostics/GelmanRubinStatSci1992.pdfCitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:3630270
Collections
- FAS Scholarly Articles [18276]
Contact administrator regarding this item (to report mistakes or request changes)