REGRESSIONS WITH BERKSON ERRORS IN COVARIATES-A NONPARAMETRIC APPROACH

被引：21

作者：

Schennach, Susanne M. ^{[1
]}

机构：

[1] Brown Univ, Dept Econ, Providence, RI 02912 USA

来源：

ANNALS OF STATISTICS | 2013年 / 41卷 / 03期

基金：

美国国家科学基金会;

关键词：

Berkson measurement error; errors in variables; instrumental variables; nonparametric inference; nonparametric maximum likelihood; INSTRUMENTAL VARIABLE ESTIMATION; PARTICULATE AIR-POLLUTION; IN-VARIABLES; NONLINEAR MODELS; IDENTIFICATION; MORTALITY;

D O I：

10.1214/13-AOS1122

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via Monte Carlo simulations as well as through an epidemiological application investigating the effect of particulate air pollution on respiratory health. These examples illustrate that Berkson errors can clearly not be neglected in nonlinear regression models and that the proposed method represents an effective remedy.

引用

页码：1642 / 1668

页数：27

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