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From the publisher's description: „. a unique and important new resource destined to become on of the most frequently consulted references in the field.“ (Mathematical Reviews, 2001 f)„. provides a comprehensive, concise coverage of statistics for engineers and scientists. I would recommend the use of this book for teaching statistics students.“ (Journal of Quality Technology, Vol. 34, No. 1, January 2002) From the publisher's description: „. a unique and important new resource destined to become on of the most frequently consulted references in the field.“ (Mathematical Reviews, 2001 f)„. provides a comprehensive, concise coverage of statistics for engineers and scientists. I would recommend the use of this book for teaching statistics students.“ (Journal of Quality Technology, Vol. 34, No. 1, January 2002)
Smoothing and Regression
Approaches, Computation, and Application
herausgegeben von Michael G. SchimekA comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression
Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis.
Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include:
For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.
Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis.
Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include:
For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.