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  • Search: subject:"Ordinary smooth"
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Year of publication
Subject
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Ordinary smooth 3 Error component 2 Asymptotic normality 1 Bounded support 1 Deconvolution 1 Measurement error model 1 Nichtparametrisches Verfahren 1 Nonparametric estimation 1 Nonparametric statistics 1 Semi-parametric 1 Semi-uniform consistency 1 Statistical distribution 1 Statistical error 1 Statistische Verteilung 1 Statistischer Fehler 1 Stochastic frontier 1 Stochastic process 1 Stochastischer Prozess 1 Theorie 1 Theory 1 deconvolution 1 errors-in-variables 1 linear processes 1 local polynomial regression 1 long-range dependence 1 ordinary smooth 1 ordinary smooth case 1 semi-uniform consistency 1 supersmooth case 1
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Online availability
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Undetermined 3 Free 2
Type of publication
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Article 3 Book / Working Paper 2
Type of publication (narrower categories)
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Article in journal 1 Aufsatz in Zeitschrift 1
Language
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Undetermined 3 English 2
Author
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Horrace, William C. 2 Parmeter, Christopher F. 2 Cai, Jun 1 Honda, Toshio 1 Horrace, William 1 Hu, Yingyao 1 Parmeter, Christopher 1 Ridder, Geert 1
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Institution
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Center for Policy Research, Maxwell School 1 Institute of Economic Research, Hitotsubashi University 1
Published in...
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Center for Policy Research Working Papers 1 Econometric Reviews 1 Global COE Hi-Stat Discussion Paper Series 1 Journal of Productivity Analysis 1 Journal of productivity analysis : an official journal of the International Society for Efficiency and Productivity Analysis 1
Source
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RePEc 4 ECONIS (ZBW) 1
Showing 1 - 5 of 5
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Density deconvolution with Laplace errors and unknown variance
Cai, Jun; Horrace, William C.; Parmeter, Christopher F. - In: Journal of productivity analysis : an official journal … 56 (2021) 2/3, pp. 103-113
Persistent link: https://www.econbiz.de/10012656574
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Nonparametric regression for dependent data in the errors-in-variables problem
Honda, Toshio - Institute of Economic Research, Hitotsubashi University - 2009
We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how...
Persistent link: https://www.econbiz.de/10008495553
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Semiparametric Deconvolution with Unknown Error Variance
Horrace, William C.; Parmeter, Christopher F. - Center for Policy Research, Maxwell School - 2008
Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about teh nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this...
Persistent link: https://www.econbiz.de/10005698391
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Semiparametric deconvolution with unknown error variance
Horrace, William; Parmeter, Christopher - In: Journal of Productivity Analysis 35 (2011) 2, pp. 129-141
Persistent link: https://www.econbiz.de/10008926063
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On Deconvolution as a First Stage Nonparametric Estimator
Hu, Yingyao; Ridder, Geert - In: Econometric Reviews 29 (2010) 4, pp. 365-396
We reconsider Taupin's (2001) Integrated Nonlinear Regression (INLR) estimator for a nonlinear regression with a mismeasured covariate. We find that if we restrict the distribution of the measurement error to a class of distributions with restricted support, then much weaker smoothness...
Persistent link: https://www.econbiz.de/10008503105
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