An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples

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.