Approximate Conditional Inference in Exponential Families via the Gibbs Sampler
作者:
JohnE. Kolassa,
MartinA. Tanner,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 426
页码: 697-702
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476796
出版商: Taylor & Francis Group
关键词: Conditional inference;Gibbs sampler;Markov chain;Monte Carlo;Saddlepoint approximations
数据来源: Taylor
摘要:
This article presents the Gibbs-Skovgaard algorithm for approximate frequentist inference. The method makes use of the double saddlepoint approximation of Skovgaard to the conditional cumulative distribution function of a sufficient statistic given the remaining sufficient statistics. This approximation is then used in the Gibbs sampler to generate a Markov chain. The equilibrium distribution of this chain approximates the joint distribution of the sufficient statistics associated with the parameters of interest conditional on the observed values of the sufficient statistics associated with the nuisance parameters. This Gibbs-Skovgaard algorithm is applied to the cases of logistic and Poisson regression.
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