Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30173
Title: Wavelet estimation in varying-coefficient partially linear regression models
Authors: Zhou, X
You, J
Keywords: Asymptotic normality
Consistency
Least-squares estimation
Partially linear regression model
Varying-coefficient
Wavelet
Issue Date: 2004
Publisher: Elsevier Science Bv
Source: Statistics and probability letters, 2004, v. 68, no. 1, p. 91-104 How to cite?
Journal: Statistics and Probability Letters 
Abstract: This paper is concerned with the estimation of a varying-coefficient partially linear regression model that is frequently used in statistical modeling. We first construct estimators of the parametric components and the error variance by a wavelet procedure and establish their asymptotic normalities under weaker conditions than those assumed in the previous literature. Then we propose appropriate estimators for the functions characterizing the nonlinear part of the model and derive their convergence rates. Furthermore, we present consistent estimators for the asymptotic (co)variances of the parametric components and error variance estimators as well. These results can be used to make asymptotically valid statistical inference.
URI: http://hdl.handle.net/10397/30173
ISSN: 0167-7152
DOI: 10.1016/j.spl.2004.01.018
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