Robust empirical Bayes tests for discrete distributions

R.J. Karunamuni, T. Liang and JunJie Wu

Journal of Nonparametric Statistics, 2008, vol. 20, issue 2, 101-113

Abstract: In this paper, we investigate the empirical Bayes (EB) linear loss two-action problem for discrete distributions. Rates of convergence of the excess risk (the regret) of the EB rules are the main interest here. Previous results on the same problem have examined EB rules in the discrete exponential family or in particular types of discrete distributions. Here, we study EB rules under very general set-up, where the distributions of the observations are not fixed. When specialised to specific distributions, however, our results reduce to similar results available in the literature for such specific distributions. We show that the rates of convergence of the proposed EB rules are of the exponential order of the form O(exp(−cdn)), where {dn} is a sequence of positive numbers decreasing to zero as n→∞, with n being the number of observations. Another distinct feature of our results is that they are stated for a class of EB rules for the present problem, whereas some particular EB rules have been studied in previous work.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485250801908371 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:20:y:2008:i:2:p:101-113

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485250801908371

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().