Economics at your fingertips  

Empirical Bayes nonparametric kernel density estimation

Alan Ker () and A.T. Ergün

Statistics & Probability Letters, 2005, vol. 75, issue 4, 315-324

Abstract: We propose using empirical Bayes on estimated density values to exploit potential similarities among a set of unknown densities. The strengths are that it allows all types of kernel estimators and does not require specification as to the form of similarity.

Keywords: Shrinkage; estimator; Ensemble; estimator (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Nithya Sathishkumar ().

Page updated 2021-05-03
Handle: RePEc:eee:stapro:v:75:y:2005:i:4:p:315-324