A nonparametric regression estimator that adapts to error distribution of unknown form
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Other versions of this item:
- Linton, Oliver & Xiao, Zhijie, 2007. "A Nonparametric Regression Estimator That Adapts To Error Distribution Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 23(3), pages 371-413, June.
- Oliver Linton & Zhijie Xiao, 2001. "A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form," STICERD - Econometrics Paper Series 419, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Linton, Oliver & Xiao, Zhijie, 2001. "A nonparametric regression estimator that adapts to error distribution of unknown form," LSE Research Online Documents on Economics 2120, London School of Economics and Political Science, LSE Library.
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Cited by:
- Wang, Dong, 2010. "Modeling epigenetic modifications under multiple treatment conditions," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1179-1189, April.
- Linton, Oliver & Xiao, Zhijie, 2019.
"Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
- Linton, O. & Xiao, Z., 2019. "Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity," Cambridge Working Papers in Economics 1907, Faculty of Economics, University of Cambridge.
- Wang, Qin & Yao, Weixin, 2012. "An adaptive estimation of MAVE," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 88-100, February.
- Yao, Weixin, 2013. "A note on EM algorithm for mixture models," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 519-526.
- Zhang, Xibin & King, Maxwell L. & Shang, Han Lin, 2014.
"A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density,"
Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 218-234.
- Xibin Zhang & Maxwell L. King & Han Lin Shang, 2013. "A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 20/13, Monash University, Department of Econometrics and Business Statistics.
- McCloud, Nadine & Parmeter, Christopher F., 2020. "Determining the Number of Effective Parameters in Kernel Density Estimation," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
- Moradi Rekabdarkolaee, Hossein & Wang, Qin, 2017. "Variable selection through adaptive MAVE," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 44-51.
- Xibin Zhang & Maxwell L. King & Han Lin Shang, 2011. "Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 10/11, Monash University, Department of Econometrics and Business Statistics.
- Chen, Yixin & Wang, Qin & Yao, Weixin, 2015. "Adaptive estimation for varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 17-31.
- Chaouch, Mohamed, 2019. "Volatility estimation in a nonlinear heteroscedastic functional regression model with martingale difference errors," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 129-148.
- De Gooijer, Jan G. & Reichardt, Hugo, 2021. "A multi-step kernel–based regression estimator that adapts to error distributions of unknown form," LSE Research Online Documents on Economics 115083, London School of Economics and Political Science, LSE Library.
More about this item
Keywords
Adaptive Estimation; Asymptotic Expansions; Efficiency; Kernel; Local Likelihood Estimation; Nonparametrie Regression;All these keywords.
JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2001-09-10 (Econometrics)
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