A note on asymptotic normality of kernel estimation for linear random fields on Z 2

Tsung-Lin Cheng, Hwai Chung Ho, Xuewen Lu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This note considers the kernel estimation of a linear random field on Z 2. Instead of imposing certain mixing conditions on the random fields, it is assumed that the weights of the innovations satisfy a summability property. By building a martingale decomposition based on a suitable filtration, asymptotic normality is proven for the kernel estimator of the marginal density of the random field.

Original languageEnglish
Pages (from-to)267-286
Number of pages20
JournalJournal of Theoretical Probability
Volume21
Issue number2
DOIs
Publication statusPublished - 2008 Jun 1

Fingerprint

Kernel Estimation
Asymptotic Normality
Random Field
Mixing Conditions
Kernel Estimator
Summability
Martingale
Filtration
Decompose
Random field
Asymptotic normality
Kernel estimation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

Cite this

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A note on asymptotic normality of kernel estimation for linear random fields on Z 2. / Cheng, Tsung-Lin; Ho, Hwai Chung; Lu, Xuewen.

In: Journal of Theoretical Probability, Vol. 21, No. 2, 01.06.2008, p. 267-286.

Research output: Contribution to journalArticle

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