Approximating bayesian inference by weighted likelihood
Copyright policy )Approximating bayesian inference by weighted likelihood
AbstractThe author proposes to use weighted likelihood to approximate Bayesian inference when no external or prior information is available. He proposes a weighted likelihood estimator that minimizes the empirical Bayes risk under relative entropy loss. He discusses connections among the weighted likelihood, empirical Bayes and James‐Stein estimators. Both simulated and real data sets are used for illustration purposes.
- York University Canada
entropy loss, weighted likelihood, James-Stein estimator, educational experiments, hierarchical Bayes, Bayesian inference, Computational problems in statistics, Statistical aspects of information-theoretic topics, Bayesian problems; characterization of Bayes procedures, Empirical decision procedures; empirical Bayes procedures, Nonparametric regression and quantile regression, empirical Bayes
entropy loss, weighted likelihood, James-Stein estimator, educational experiments, hierarchical Bayes, Bayesian inference, Computational problems in statistics, Statistical aspects of information-theoretic topics, Bayesian problems; characterization of Bayes procedures, Empirical decision procedures; empirical Bayes procedures, Nonparametric regression and quantile regression, empirical Bayes
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