指数分布族参数的渐近最优与可容许的经验Bayes估计

在平方损失下 ,构造了指数族 { f(x|λ) =λe-λx,λ >0 ,x >0 }的参数λ的渐近最优与可容许的经验Bayes估计 ,即δn=(n +u + 1n1φ(n) + 1) β1+ βX,其中X1,X2 ,…Xn(历史样本 )和X(当前样本 )独立同分布于 f(x) ,Sn= ni=11n(1+ βXi) ,φ(n) =1n(Sn+ 1n(1+ βX) +v- 1) ,u >0 ,v >0 ,β >0 (已知 )为任意的实数 ,并证明了该估计的收敛速度为O(n- 1)。     

渐近最优的经验贝叶斯决策

本文在加权线性损失下讨论一类广义指数分布刻度参数的经验贝叶斯检验问题.利用核密度估计函数构造单调的经验贝叶斯检验函数,在适当的条件下证明所构造的检验函数的渐近最优性并获得其收敛速度.该收敛速度可以任意接近O(n-1).最后,给出一个例子用以验证本文的主要结果是合理的.     

A bayes procedure for selecting the population with the largestpth quantile

Summary The Bayes method is seldom applied to nonparametric statistical problems, for the reason that it is hard to find mathematically tractable prior distributions on a set of probability measures. However, it is found that the Dirichlet process generates randomly a family of probability distributions which can be taken as a family of prior distributions for an application of the Bayes method to such problems. This paper presents a Bayesian analysis of a nonparametric problem of selecting a distribution with the largestpth quantile value, fromk≧2 given distributions. It is assumed a priori that the given distributions have been generated from a Dirichlet process. This work was supported by the U.S. Office of Naval Research under Contract No. 00014-75-C-0451.     

连续型单参数指数族的经验Bayes检验

本文讨论了连续型单参数指数族的经验Bayes检验问题 .利用核估计方法构造了EB检验函数并获得了它的收敛速度 .     

The empirical bayes rules with floating optimal sample size for exponential conditional distributions

Summary We consider the empirical Bayes solution in such a situation where the sample size is successively determined by a rule which includes the Bayes risks and the observation costs. The empirical Bayes floating optimal sample size depends on current as well as on previous information assumed to be collected from earlier performances of similar decisions. The sampling is done from an exponential conditional distribution, with a single parameter. The proofs, which show the asymptotic optimality of the empirical Bayes solution, are presented for a hypotheses-testing problem. A straight generalization to a multiple decision problem is also given.