Title :
Optimal rates of empirical Bayes tests for a positive exponential family in the case of negatively associated samples
Author :
Jiaqing, Chen ; Cihua, Liu
Author_Institution :
Department of Statistics, Wuhan University of Technology, 430070, China
Abstract :
By using the kernel-type density estimation in the case of identically distributed and negatively associated (NA) samples, the monotone empirical Bayes test(EBT) rules for the parameter of a positive exponential family in the case of NA samples are constructed 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)under a somewhat weaker condition.
Keywords :
Artificial neural networks; Convergence; Estimation; Planning; Space technology; Testing; asymptotic optimality; convergence rates; empirical Bayes test; negatively associated samples;
Conference_Titel :
Information Science and Engineering (ICISE), 2010 2nd International Conference on
Conference_Location :
Hangzhou, China
Print_ISBN :
978-1-4244-7616-9
DOI :
10.1109/ICISE.2010.5690759
