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Smoothed influence function : another view at robust nonparametric regression
Tamine, Julien - 2001
In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...
Persistent link: https://www.econbiz.de/10009626684
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A nonparametric regression estimator that adapts to error distribution of unknown form
Linton, Oliver; Xiao, Zhijie - 2001
We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is asymptotically equivalent to the infeasible local maximum...
Persistent link: https://www.econbiz.de/10009613602
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Non-uniformity of job-matching in a transition economy : a nonparametric analysis for the Czech Republic
Profit, Stefan; Sperlich, Stefan - 1998
We consider problems in modelling job-matching in the Czech Republic during the transition to a market economy. Special … interest is devoted to functional form considerations and the analysis of returns to scale of the matching function. This … explorative study aims to shed some light into the black-box of the matching technology by applying nonparametric estimation …
Persistent link: https://www.econbiz.de/10009574874
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An adaptive, rate-optimal test of a parametric model against a nonparametric alternative
Horowitz, Joel; Spokojnyj, Vladimir G. - 1999
We develop a new test of a parametric model of a conditional mean function against a nonparametric alternative. The test adapts to the unknown smoothness of the alternative model and is uniformly consistent against alternatives whose distance from the parametric model converges to zero at the...
Persistent link: https://www.econbiz.de/10009579179
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A nonparametric test for the stationary density
Neumann, Michael H.; Paparoditis, Efstathios - 1998
We propose a nonparametric test for checking parametric hypotheses about the stationary density of weakly dependent observations. The test statistic is based on the L2-distance between a nonparametric and a smoothed version of a parametric estimate of the stationary density. It can be shown that...
Persistent link: https://www.econbiz.de/10009578009
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Two adaptive rates of convergence in pointwise density estimation
Butucea, Cristina - 1999
We consider density pointwise estimation and look for best attainable asymptotic rates of convergence. The problem is adaptive, which means that the regularity parameter, β, describing the class of densities, varies in a set B. We shall consider, successively, two classes of densities, issued...
Persistent link: https://www.econbiz.de/10009579174
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Error reduction in density estimation under shape restrictions
Rychlik, Tomasz - 1999
For the problems of nonparametric estimation of nonincreasing and symmetric unimodal density functions with bounded supports we determine the projections of estimates onto the convex families of possible parent densities with respect to the weighted integrated squared error. We also describe the...
Persistent link: https://www.econbiz.de/10009579180
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Numerical results concerning a sharp adaptive density estimator
Butucea, Cristina - 1999
We give here a simulation study of a density estimator, issued from sharp adaptive estimation. This nonparametric estimator was previously proved to have interesting theoretical properties. In this paper we describe the method and apply it successfully to i.i.d. simulated data issued from...
Persistent link: https://www.econbiz.de/10009580480
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Estimating state-price densities with nonparametric regression
Huynh, Kim; Kervella, Pierre; Zheng, Jun - 2002
Persistent link: https://www.econbiz.de/10009624851
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Nonparametric estimation of scalar diffusions based on low frequency data is ill-posed
Gobet, Emmanuel; Hoffmann, Marc; Reiß, Markus - 2002
We study the problem of estimating the coefficients of a diffusion (Xl, t 2:: 0); the estimation is based on discrete data Xn . . n = 0, 1, ... ,N. The sampling frequency delta t is constant , and asymptotics arc taken at the number of observations tends to infinity. We prove that the problem of...
Persistent link: https://www.econbiz.de/10009626683