Wavelet estimation in varying-coefficient partially linear regression models
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.Download Info
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Bibliographic Info
Article provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 68 (2004)
Issue (Month): 1 (June)
Pages: 91-104
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Related research
Keywords: Partially linear regression model Varying-coefficient Wavelet Least-squares estimation Asymptotic normality Consistency;References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- You, Jinhong & Chen, Gemai, 2006. "Estimation of a semiparametric varying-coefficient partially linear errors-in-variables model," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 324-341, February.
- Zhang, Weiwei & Li, Gaorong & Xue, Liugen, 2011. "Profile inference on partially linear varying-coefficient errors-in-variables models under restricted condition," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3027-3040, November.
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