An Update on Measurement Error Modeling

被引：0

作者：

Li, Mushan ^{[1
]}

Ma, Yanyuan ^{[1
]}

机构：

[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA

来源：

ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION | 2024年 / 11卷

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Berkson error; classical error; errors in variables; measurement errors; IN-VARIABLES; HETEROSCEDASTIC MEASUREMENT; SEMIPARAMETRIC ESTIMATORS; NONPARAMETRIC REGRESSION; OPTIMAL RATES; CONVERGENCE;

D O I：

10.1146/annurev-statistics-040722-043616

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The issues caused by measurement errors have been recognized for almost 90 years, and research in this area has flourished since the 1980s. We review some of the classical methods in both density estimation and regression problems with measurement errors. In both problems, we consider when the original error-free model is parametric, nonparametric, and semiparametric, in combination with different error types. We also summarize and explain some new approaches, including recent developments and challenges in the high-dimensional setting.

引用

页码：279 / 296

页数：18

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