Measurement, Regression, and Calibration

Hardcover | November 1, 1994

byPhilip J. Brown

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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 specificallydeveloped 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 patternrecognition.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 regressionand 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.

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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 specificallydeveloped for spectroscopy. The other chapter...

Philip J. Brown is at University of Liverpool.

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Format:HardcoverDimensions:210 pages, 9.21 × 6.14 × 0.67 inPublished:November 1, 1994Publisher:Oxford University Press

The following ISBNs are associated with this title:

ISBN - 10:0198522452

ISBN - 13:9780198522454

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Table of Contents

Introduction1. Simple linear regression2. Multiple regression and calibration3. Regularized multiple regression4. Multivariate calibration5. Regession on curves6. Non-linearity and selection7. Pattern recognitionA. Distribution theoryB. Conditional inferenceC. Regularization dominanceE. Partial least-squares algorithmBibliographyIndex

Editorial Reviews

`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