Optimal asymptotic quadratic errors of density estimators on random fields

Gérard Biau

Statistics & Probability Letters, 2002, vol. 60, issue 3, 297-307

Abstract: Let denote the integer lattice points in the N-dimensional Euclidean space. Kernel estimation of the multivariate density of a random field indexed by is investigated. The loss between the estimator and the unknown density is measured by means of mean squared and mean integrated squared errors. Under mild mixing conditions, we show that the kernel density estimator has exactly the same asymptotic error as in the i.i.d. case.

Keywords: Kernel; density; estimation; Random; field; Optimality; Mixing (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(02)00305-X
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:60:y:2002:i:3:p:297-307

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 ().