ABSTRACT
Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.
The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.
Features:
- Provides an account of past development and modern advancement concerning measurement error problems
- Highlights the challenges induced by error-contaminated data
- Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error
- Describes state-of-the-art strategies for conducting in-depth research
TABLE OF CONTENTS
part I|52 pages
Introduction
chapter 2Chapter 1|34 pages
Measurement Error Models - A Brief Account of Past Developments and Modern Advancements
part II|44 pages
Identifiability and Estimation
chapter Chapter 5|12 pages
Using Instrumental Variables to Estimate Models with Mismeasured Regressors
part III|86 pages
General Methodology
part IV|136 pages
Nonparametric Inference
chapter Chapter 11|24 pages
Nonparametric Deconvolution by Fourier Transformation and Other Related Approaches
part V|108 pages
Applications
chapter Chapter 16|16 pages
Mixed Effects Models with Measurement Errors in Time-Dependent Covariates
part VI|78 pages
Other Features
part VII|50 pages
Bayesian Analysis