Handbook of Measurement Error Models.

✍ Scribed by Grace Y. Yi, Aurore Delaigle, and Paul Gustafson

Publisher: CRC Press
Year: 2021
Tongue: English
Leaves: 592
Category: Library

No coin nor oath required. For personal study only.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
Part I: Introduction
1. Measurement Error Models - A Brief Account of Past Developments and Modern Advancements
2. The Impact of Unacknowledged Measurement Error
Part II: Identifiability and Estimation
3. Identifiability in Measurement Error Models
4. Partial Learning of Misclassification Parameters
5. Using Instrumental Variables to Estimate Models with Mismeasured Regressors
Part III: General Methodology
6. Likelihood Methods with Measurement Error and Misclassification
7. Regression Calibration for Covariate Measurement Error
8. Conditional and Corrected Score Methods
9. Semiparametric Methods for Measurement Error and Misclassification
Part IV: Nonparametric Inference
10. Deconvolution Kernel Density Estimation
11. Nonparametric Deconvolution by Fourier Transformation and Other Related Approaches
12. Deconvolution with Unknown Error Distribution
13. Nonparametric Inference Methods for Berkson Errors
14. Nonparametric Measurement Errors Models for Regression
Part V: Applications
15. Covariate Measurement Error in Survival Data
16. Mixed Effects Models with Measurement Errors in Time-Dependent Covariates
17. Estimation in Mixed-effects Models with Measurement Error
18. Measurement Error in Dynamic Models
19. Spatial Exposure Measurement Error in Environmental Epidemiology
Part VI: Other Features
20. Measurement Error as a Missing Data Problem
21. Measurement Error in Causal Inference
22. Measurement Error and Misclassification in Meta-Analysis
Part VII: Bayesian Analysis
23. Bayesian Adjustment for Misclassification
24. Bayesian Approaches for Handling Covariate Measurement Error
Author Index
Subject Index

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