On an Asymptotic Theory of Conditional and Unconditional Coverage Probabilities of Empirical Bayes Confidence Intervals
On an Asymptotic Theory of Conditional and Unconditional Coverage Probabilities of Empirical Bayes Confidence Intervals
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经验贝叶斯置信区间的条件和无条件覆盖概率的渐近理论
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
P. Lahiri
中科院分区：
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作者：
G. Datta;M. Ghosh;David D. Smith;P. Lahiri
经验贝叶斯（EB）方法在统计学中得到了广泛的应用。然而，EB置信区间的构建仍然非常有限。继考克斯（Cox）（1975）、Hill（1990）和Carlin & Gelfand（1990，1991）之后，我们考虑EB置信区间，对其进行调整，以使实际覆盖概率渐进满足目标覆盖概率，直至达到二阶。我们考虑无条件和有条件的覆盖率，条件是相对于一个辅助统计。
Empirical Bayes (EB) methodology is now widely used in statistics. However, construction of EB confidence intervals is still very limited. Following Cox (1975 ), Hill (1990 ) and Carlin & Gelfand (1990 , 1991 ), we consider EB confidence intervals, which are adjusted so that the actual coverage probabilities asymptotically meet the target coverage probabilities up to the second order. We consider both unconditional and conditional coverage, conditioning being done with respect to an ancillary statistic.