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