Measurement, Regression, and Calibration [Hardcover]

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 Calibrationwill be a valuable addition to the bookshelves of professionals and advanced students in statistics and other pertinent fields.

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

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 arlãb