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

Author

Minna Shao

Author_Institution

Sch. of Math. & Comput. Sci., Ningxia Univ., Yinchuan, China

fYear

2013

fDate

23-25 July 2013

Firstpage

648

Lastpage

652

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on

Conference_Location

Shenyang

Type

conf

DOI

10.1109/FSKD.2013.6816276

Filename

6816276