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Measurement, Regression, and Calibration : Oxford Statistical Science Series - Philip J. Brown

Measurement, Regression, and Calibration

Oxford Statistical Science Series


Published: 6th January 1994
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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.

`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

Introduction 1: Simple linear regression 2: Multiple regression and calibration 3: Regularized multiple regression 4: Multivariate calibration 5: Regession 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

ISBN: 9780198522454
ISBN-10: 0198522452
Series: Oxford Statistical Science Series
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 210
Published: 6th January 1994
Publisher: Oxford University Press
Country of Publication: GB
Dimensions (cm): 24.1 x 16.2  x 1.7
Weight (kg): 0.49