Measurement, Regression, and Calibration
Series: Oxford Statistical Science Series; 12;
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Product details:
- Publisher Clarendon Press
- Date of Publication 6 January 1994
- ISBN 9780198522454
- Binding Hardback
- No. of pages210 pages
- Size 241x162x17 mm
- Weight 474 g
- Language English
- Illustrations line figures, tables 0
Categories
Short description:
This book explains the statistical theory behind a range of regression problems in which one set of variables is predicted from another. The applications are from industry and medicine where the researchers are using sophisticated electronic measuring devices that are capable of monitoring very many variables.
MoreLong description:
The book starts with a range of examples and develops techniques progressively, starting with standard least squares prediction of a single variable from another and moving onto shrinkage techniques for multiple variables. Chapters 6 and 7 refer mostly to methods that have been specifically developed for spectroscopy. The other chapters are quite general in their applicability. Likelihood and Bayesian inference features strongly, the latter allowing flexible analysis of a wide range of multivariate regression problems. The last chapter presents some Bayesian approaches to pattern recognition.
For teaching purposes instructors may find particular chapters sufficiently self contained to recommend in isolation as reference or reading material. For example chapter 4 gives an in depth development of a range of shrinkage techniques. including partial least squares regression, ridge regression and principal components regression; together with discussion of the recently proposed continuum regression. Chapter 8 on pattern recognition may also be of us by itself in courses on multivariate analysis and Bayesian Statistics.
This well-written research monograph deals with regression problems that are not commonly covered in statistical methodology courses but that often arise in applications ... Technometrics readers will find much of interest and use in Brown's monograph, not the least of which are expositions of the author's own very considerable contributions to the topics under discussion. Brown is very good at presenting apparently different approaches in a unifying framework that both brings out common features and clarifies differences ... a welcome contribution ... I enjoyed reading this book and recommend it heartily.
Table of Contents:
Introduction
Simple linear regression
Multiple regression and calibration
Regularized multiple regression
Multivariate calibration
Regession on curves
Non-linearity and selection
Pattern recognition
Distribution theory
Conditional inference
Regularization dominance
Partial least-squares algorithm
Bibliography
Index
