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√n-uniformly consistent density estimation in nonparametric regression models
被引:19
|作者:
Escanciano, Juan Carlos
[2
]
Jacho-Chavez, David T.
[1
]
机构:
[1] Emory Univ, Dept Econ, Atlanta, GA 30322 USA
[2] Indiana Univ, Dept Econ, Bloomington, IN 47405 USA
关键词:
Density estimation;
Kernel smoothing;
U-processes;
SEMIPARAMETRIC ESTIMATION;
MARGINAL DENSITY;
CONVERGENCE;
CHOICE;
RATES;
D O I:
10.1016/j.jeconom.2011.09.017
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
The paper introduces a root n-consistent estimator of the probability density function of the response variable in a nonparametric regression model. The proposed estimator is shown to have a (uniform) asymptotic normal distribution, and it is computationally very simple to calculate. A Monte Carlo experiment confirms our theoretical results. The results derived in the paper adapt general U-processes theory to the inclusion of infinite dimensional nuisance parameters. (C) 2011 Elsevier B.V. All rights reserved.
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页码:305 / 316
页数:12
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