√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

来源：

JOURNAL OF ECONOMETRICS | 2012年 / 167卷 / 02期

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.

引用

页码：305 / 316

页数：12

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