A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models-Reference-Cited by-同舟云学术

A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models

Published:2004-03 Issue:1 Volume:60 Page:172-181
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Freedman Laurence S.¹,Fainberg Vitaly²,Kipnis Victor²,Midthune Douglas²,Carroll Raymond J.³

Affiliation:

1. Department of Mathematics and Statistics, Bar Ilan University, Ramat Gan 52900, Israel

2. Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, MSC 7354, Bethesda, Maryland 20892‐7354, U.S.A.

3. Department of Statistics, Texas A&M University, College Station, Texas 77843‐3143, U.S.A.

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0006-341X.2004.00164.x

Reference18 articles.

1. Data

2. Measurement Error in Nonlinear Models

Cited by 47 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recommendations on statistical approaches to account for dose uncertainties in radiation epidemiologic risk models;International Journal of Radiation Biology;2024-07-26

2. A generalisation of the method of regression calibration;Scientific Reports;2023-09-13

3. Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington’s disease subjects;Journal of the Royal Statistical Society Series C: Applied Statistics;2023-09-13

4. References;Multiple Imputation and its Application 2e;2023-07-21

5. Detecting and correcting for heteroscedasticity in the presence of measurement error;Communications in Statistics - Simulation and Computation;2023-03-28