Measurement, Regression, and Calibration

By: Philip J. Brown

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Hardcover | 6 January 1994

At a Glance

Hardcover
212 Pages

Dimensions(cm)
24.1 x 16.2 x 1.7

Hardcover

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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.

Industry Reviews

`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.' Leon Jay Gleser, University of Pittsburgh `Whatever your preferences, there is valuable understanding to be gained by formulating these problems (or indeed any problem) in a Bayesian framework, and one of the strong points of this book is that it always looks for and often finds that understanding ... a valuable reference.' Statistical Methods in Medical Research, Vol. 4, 1995

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