Local Walsh-average regression for semiparametric varying-coefficient models
AbstractThis work is concerned with robust estimation in a semiparametric varying-coefficient partially linear model when the underlying error distribution deviates from a normal distribution. We develop a robust estimator by minimizing a locally Walsh-average-based loss function. We show theoretically that the proposed estimator is highly efficient across a wide spectrum of distributions. Its asymptotic relative efficiency with respect to the least-squares-based method is closely related to that of the signed-rank Wilcoxon test in comparison with the t-test. Both the theoretical and the numerical results demonstrate that the performance of the new approach is at least comparable to those of existing works.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 82 (2012)
Issue (Month): 10 ()
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Feng, Long & Zou, Changliang & Wang, Zhaojun, 2012. "Local Walsh-average regression," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 36-48.
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- Zhang, Wenyang & Lee, Sik-Yum, 2000. "Variable Bandwidth Selection in Varying-Coefficient Models," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 116-134, July.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
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