摘要
在弱平稳α-混合样本下利用概率密度函数的核估计构造了伽玛分布族参数的经验Bayes(EB)检验函数,并获得了它的渐进最优(a.o.)性.在适当的条件下证明了所提出的EB检验函数收敛速度可任意接近O(n^(-1/2)).
By using the kernel-type density estimation in the case of weak stationary a mixing random samples, the empirical Bayes two-sided test rules for the gamma distribution family are constructed, and the asymptotically optimal property is obtained. It is shown that 1 the convergence rates of the proposed EB test rules can arbitrarily close to O（n-1/2）under suitable conditions.
出处
《数学的实践与认识》
CSCD
北大核心
2010年第4期183-187,共5页
Mathematics in Practice and Theory
基金
广东海洋大学科研项目(0612163
E09230)
2008年广东省哲学社会科学十一规划项目(08YO01)
关键词
弱平稳
α-混合样本
经验BAYES检验
渐进最优性
收敛速度
weak stationary
s-mixing random samples
empirical Bayes test
asymptotic optimality
convergence rates.
