Measurement, Regression, and Calibration

P. J. Brown

A Clarendon Press Publication

Oxford Statistical Science Series

Measurement, Regression, and Calibration

P. J. Brown

A Clarendon Press Publication

Oxford Statistical Science Series

Description

With an abundance of helpful examples, this text expertly presents the essentials of measurement, regression, and calibration. The book develops the fundamentals and underlying theories of key techniques in a clear, step-by-step progression, starting with standard least squares prediction of a single variable and moving on to shrinkage techniques for multiple variables. Self-contained chapters discuss methods that have been specifically developed for spectroscopy, likelihood and Bayesian inference (which may be applied to a wide range of multivariate regression problems), and Bayesian approaches to pattern recognition, among other topics. Ideal for instruction as well as for reference, Measurement, Regression, and Calibration will be a valuable addition to the bookshelves of professionals and advanced students in statistics and other pertinent fields.

Measurement, Regression, and Calibration

P. J. Brown

A Clarendon Press Publication

Oxford Statistical Science Series

Introduction
1. Simple linear regression
2. Multiple regression and calibration
3. Regularized multiple regression
4. Multivariate calibration
5. Regression on curves
6. Non-linearity and selection
7. Pattern recognition
A. Distribution theory
B. Conditional inference
C. Regularization dominance
E. Partial least-squares algorithm
Bibliography
Index

Measurement, Regression, and Calibration

P. J. Brown

A Clarendon Press Publication

Oxford Statistical Science Series

Reviews and Awards

"Working statisticians, particularly chemometricians, will find it useful for summarizing and clarifying the choices of models and procedures available to them for inference problems that they often encounter and for which there are few alternative references. Academic statisticians will want to use this monograph as supplementary reading for graduate-level courses in regression and as a quick entry for finding open questions requiring research." -- Technometrics

"Several numerical examples with numerous figures are helpful in understanding the motivation and the theory developed here. A bibliography of selected references and appendices concerning distribution theory in multivariate analysis are also provided. The book deals with various topics which are not discussed in detail in the literature on regression models. Furthermore, the emphasis is on calibration and the Bayesian approach. Such features of the book make it a very useful reference." -- Mathematical Reviews

"Brown provides a succinct but very clear overview of 'regularized' regression. . .a valuable work for researchers and statisticians. . ." --Choice

"This book deals with sufficiently sophisticated theory and applications of regression and calibration with an extensive emphasis on controlled calibration. It is lucidly written and provides a wealth of information in the course of a mere 169 pages. The book will also be found very useful as a reference text by practitioners."--SIAM Review

"[An] outstanding job. . .in describing approaches for building less than full rank models. . .I recommend this book highly."--Journal of Quality Technology