摘要
在加权“线性损失”下讨论了负相伴样本情形连续型单参数指数族参数的经验Bayes(EB)检验问题.利用概率密度函数的核估计构造了参数的经验Bayes检验函数,并获得了它的渐近最优(a.o.)性,在适当的条件下证明了所提出的经验Bayes检验函数的收敛速度可任意接近O(-n 1/2).
By using the kernel-type density estimation in the case of identically distributed and negatively associated samples, the empirical Bayes test rules for the continuous one-parameter exponential family are constructed under weighed linear loss function, and the asymptotically optimal property is obtained. It is shown that the convergence rates of the proposed EB test rules can arbitrarily close to O(n^-1/2) under suitable conditions.
出处
《纯粹数学与应用数学》
CSCD
北大核心
2007年第1期11-16,共6页
Pure and Applied Mathematics
基金
国家自然科学基金资助项目(10301011)
关键词
经验BAYES检验
渐近最优性
收敛速度
负相伴样本
empirical Bayes test, asymptotic optimality, convergence rates, negatively associated samples