Measurement error in nonlinear models
Author(s)
Bibliographic Information
Measurement error in nonlinear models
（Monographs on statistics and applied probability, 63）
Chapman & Hall/CRC, 1998
Available at / 1 libraries
-
No Libraries matched.
- Remove all filters.
Note
Reprint. Originally published: London : Chapman & Hall, 1995
Includes bibliographical references (p. [280]-297) and indexes
Description and Table of Contents
Description
This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.
Table of Contents
Preface
Guide to Notation
1. Introduction
2. Regression and Attenuation
3. Regression Calibration
4. Simulation Extrapolation
5. Instrumental Variables
6. Functional Methods
7. Likelihood and Quasilikelihood
8. Bayesian Methods
9. Semiparametric Methods
10. Unknown Link Functions
11. Hypothesis Testing
12. Density Estimation and Nonparametric Regression
13. Response Variable Error
14. Other Topics
Appendix: Fitting Methods and Models
References
Author Index
Subject Index
by "Nielsen BookData"