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Bootstrap methods using prior information

1986; Oxford University Press; Volume: 73; Issue: 1 Linguagem: Inglês

10.1093/biomet/73.1.77

1464-3510

Dennis D. Boos, John F. Monahan,

Statistical Methods and Inference

Bayesian analysis is subject to the same kinds of misspeciflcation problems which motivate the robustness and nonparametric literature. We present a method of incorporating prior information which performs well without direct knowledge of the error distribution. This is accomplished by replacing the likelihood in Bayes's formula by a bootstrap estimate of the sampling density of a robust estimator. The flexibility of the method is illustrated by examples, and its performance relative to standard Bayesian techniques is evaluated in a Monte Carlo study.