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Measurement, Regression and Calibration 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.
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
D. 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 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
