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Objective priors for the bivariate normal model
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DOI：10.1214/07-AOS501.png)
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摘要
En 中文
Study of the bivariate normal distribution raises the full range of issues involving objective Bayesian inference, including the different types of objective priors (e.g., Jeffreys, invariant, reference, matching), the different modes of inference (e.g., Bayesian, frequentist, fiducial) and the criteria involved in deciding on optimal objective priors (e.g., ease of computation, frequentist performance, marginalization paradoxes). Summary recommendations as to optimal objective priors are made for a variety of inferences involving the bivariate normal distribution. In the course of the investigation, a variety of surprising results were found, including the availability of objective priors that yield exact frequentist inferences for many functions of the bivariate normal parameters, including the correlation coefficient.
Keyword:
reference priors
matching priors
Jeffreys priors
right-Haar prior
fiducial inference
frequentist coverage
marginalization paradox
rejection sampling
constructive posterior distributions
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3.7
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分享收藏Posterior propriety and admissibility of hyperpriors in normal hierarchical models正常分层模型中先验的后验性和可容许性.png)
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ANNALS OF STATISTICSIF3.7
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