Polynomial regression with heteroscedastic measurement errors in both axes: Estimation and hypothesis testing

被引：5

作者：

Cheng, Chi-Lun ^{[1
]}

Tsai, Jia-Ren ^{[2
]}

Schneeweiss, Hans ^{[3
]}

机构：

[1] Acad Sinica, Inst Stat Sci, 128 Sect 2,Acad Rd, Taipei 115, Taiwan

[2] Fu Jen Catholic Univ, Dept Stat & Informat Sci, Taipei, Taiwan

[3] Ludwig Maximilians Univ Munchen, Inst Stat, Munich, Germany

来源：

STATISTICAL METHODS IN MEDICAL RESEARCH | 2019年 / 28卷 / 09期

关键词：

Adjusted least squares; equation error; heteroscedasticity; hypothesis testing; measurement error model; polynomial model; VARIABLES; MODEL;

D O I：

10.1177/0962280218782715

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

This article investigates point estimation and hypothesis testing in a polynomial regression model with heteroscedastic measurement errors present in both response and regressor variables. For point estimation, the adjusted least squares method and its modifications are developed. These methods can treat both functional and structural models, and models with or without an equation error. For hypothesis testing, the Wald-type and score-type tests are discussed. Their performance is investigated in a simulation study. Applications of these methods are also illustrated with real datasets.

引用

页码：2681 / 2696

页数：16

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