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

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

Content Provider	IEEE Xplore Digital Library
Author	Minna Shao
Copyright Year	2013
Description	Author affiliation: Sch. of Math. & Comput. Sci., Ningxia Univ., Yinchuan, China (Minna Shao)
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.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	648
Ending Page	652
File Size	257782
Page Count	5
File Format	PDF
ISBN	9781467352536
DOI	10.1109/FSKD.2013.6816276
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	nonexponential distribution Linear regression empirical Bayes test Estimation Educational institutions Density functional theory Random variables convergence rate kernel estimation Convergence Testing Negative Quadrant Dependent samples
Content Type	Text
Resource Type	Article

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