Local Walsh-average regression for semiparametric varying-coefficient models
This 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.
Volume (Year): 82 (2012)
Issue (Month): 10 ()
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References listed on IDEAS
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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.
- Zhang, Wenyang & Lee, Sik-Yum & Song, Xinyuan, 2002. "Local Polynomial Fitting in Semivarying Coefficient Model," Journal of Multivariate Analysis, Elsevier, vol. 82(1), pages 166-188, July.
- 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.
- repec:cup:cbooks:9780521780506 is not listed on IDEAS
- Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
- repec:cup:cbooks:9780521785167 is not listed on IDEAS
- Wang, Lan & Kai, Bo & Li, Runze, 2009. "Local Rank Inference for Varying Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1631-1645.
- Bo Kai & Runze Li & Hui Zou, 2010. "Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 49-69.
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