Title :
An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples
Author_Institution :
Sch. of Math. & Comput. Sci., Ningxia Univ., Yinchuan, China
Abstract :
The empirical Bayes (EB) test problem for parameters of a nonexponential distribution family is investigated with Negative Quadrant Dependent (NQD) random samples. By using the kernel-type density estimation method, the EB test decision rules of parameters are constructed. The asymptotically optimal property and convergence rates for the EB test decision rules are obtained under some suitable conditions. Finally, an example satisfying the conditions of the theorem is given.
Keywords :
Bayes methods; convergence; exponential distribution; statistical testing; EB test decision rules; NQD; asymptotically optimal property; convergence rates; empirical Bayes test; kernel-type density estimation method; negative quadrant dependent random samples; nonexponential distribution family; Convergence; Density functional theory; Educational institutions; Estimation; Linear regression; Random variables; Testing; Negative Quadrant Dependent samples; convergence rate; empirical Bayes test; kernel estimation; nonexponential distribution;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/FSKD.2013.6816276
