Bootstrap Methods Using Prior Information
提出一种将先验信息融入自助法的方法，用稳健估计量的抽样密度的自助估计替代贝叶斯公式中的似然，避免了对误差分布的直接假设，并通过蒙特卡洛研究评估其表现。
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.