Reviews:
'...this is a useful book and one well worth dipping into
for those concerned with regression modelling.' The
Statistician
'I would recommend that this is an essential book for any
library and for anyone with an interest in the theory of measurement
error modelling.' Journal of Applied Statistics
'Clearly written and attractively laid out, and would be excellent
for a graduate seminar or as a reference book. Its strengths
lie in a careful exposition of technicalities, an important
achievement in this area where the literature is intricate,
complex and multi-faceted.' Short Book Reviews
'The book is greatly to be welcomed, for it will be very useful;
I am glad to have a copy on my shelves.' Biometrics
'A very thorough and well written book on nearly all aspects
of the use of measurement error models in regression. For
somebody interested in doing methodological research on a
subject related to measurement error models, the book is a
must.' Statistics in Medicine
Key Features:
- Exercises at the end of each chapter
- Non-mathematical coverage of important area of statistics
- Practical approach to measurement error models.
Description:
Statistical Regression with Measurement Error is a general
survey of the theory of measurement error models (errors-in-variables
models) including the functional, structural and ultrastructural
models. It emphasizes the ideas and practical implications
of the theory in a style which avoids the traditionally difficult
theorem-proof format and instead adopts a more accessible
approach. Topics include model identifiability, parameter
estimation, confidence intervals, asymptotic theory and computational
methods. Suitable for graduate statisticians, this book is
accessible to readers that have a background in probability
and theoretical statistics. It does not require knowledge
of measure theory.
Readership:
Graduate students with a background in statistics.
Contents:
Preface
1. Introduction to linear measurement error models
2. Properties of estimates and predictors
3. Comparing model assumptions and modifying least squares
4. Alternative approaches to the measurement error model
5. Linear measurement error model with vector explanatory
variables
6. Polynominal measurement error models
7. Robust estimation in measurement error models
8. Additional topics
Appendix A Identification in measurement error models
Bibliography
Author index
Subject index
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