2008Unpublished venueRequires access

Consistency of Wavelet Estimator of Regression Function under ρ-Mixing Assumptions

WU Li-sha

Open publisher page 0 citations

Abstract

Consider a nonparametric regression model Yi=g(ti) +ei(1≤i≤n) ,where {ti} are fixed design points ,g is an un-knownfunction.Inthis paper ,let {ei} beρ-mixing dependent stationary sequences . Under suitable regularity conditions ,themean consistency of order r ,consistency,and strong consistency of wavelet estimator of g are obtained.

About this research paper

What this paper is about

Consider a nonparametric regression model Yi=g(ti) +ei(1≤i≤n) ,where {ti} are fixed design points ,g is an un-knownfunction.Inthis paper ,let {ei} beρ-mixing dependent stationary sequences . Under suitable regularity conditions ,themean consistency of order r ,consistency,and strong consistency of wavelet estimator of g are obtained.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Consider a nonparametric regression model Yi=g(ti) +ei(1≤i≤n) ,where {ti} are fixed design points ,g is an un-knownfunction.Inthis paper ,let {ei} beρ-mixing dependent stationary sequences . Under suitable regularity conditions ,themean consistency of order r ,consistency,and strong consistency of wavelet estimator of g are obtained.

Key concepts: Consistency (knowledge bases), Estimator, Strong consistency, Mixing (physics), Regression function, Mathematics, Wavelet, Nonparametric regression

Related papers

Back to paper searchBrowse research topicsOriginal source
Consistency of Wavelet Estimator of Regression Function under ρ-Mixing Assumptions — Research Paper | ScholarLens