EconPapers    
Economics at your fingertips  
 

A note on deconvolution density estimation

Prakash Patil

Statistics & Probability Letters, 1996, vol. 29, issue 1, 79-84

Abstract: We consider the problem of estimating the unknown common density f of unobservable independent random variables Xi from observable independent random variables Yij, where conditional density of Yij given Xi, is of the form gYijXi(y) = g(y - Xi; = 1,...,n and J = 1,...,m. It is shown that if E[Y2ijXi] is finite and m grows sufficiently fast then mean square error of the kernel estimator of f converges to zero with usual rate of nonparametric density estimators based on a random sample from a density that is to be estimated. This is in contrast to the case of fixed m.

Keywords: Mean; square; error; Mixing; density; Multiple; observations; Noise; level; Nonparametric; density; estimation (search for similar items in EconPapers)
Date: 1996
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(95)00158-1
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: https://EconPapers.repec.org/RePEc:eee:stapro:v:29:y:1996:i:1:p:79-84

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
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 Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:stapro:v:29:y:1996:i:1:p:79-84