Author(s): David Birkes (Author), Dr. Yadolah Dodge (Author)
Publisher: Wiley
Publication Date: 5 Aug. 1993
Edition: 1st
Language: English
Print length: 240 pages
ISBN-10: 9780471568810
ISBN-13: 9780471568810
Book Description
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts “.an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models.” –Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis–with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild “.[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process.” –Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models –growth, compartmental, and multiphase –and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge “The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects.” –Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
Editorial Reviews
From the Inside Flap
This book provides an introduction to five of the most popular alternatives to least-squares regression. Most modern textbooks on regression give brief discussions of a few alternative methods, and specialized books exist on particular methods, but, until now, such a variety of methods have not been presented within a single book. In addition to a review of least-squares regression, Alternative Methods of Regression includes coverage of:
Least-absolute-deviations regression
Robust M-regression
Nonparametric rank-based regression
Bayesian regression
Ridge regression
Each method has a chapter devoted to it, describing how regression estimates and tests are calculated and why the method makes sense. Each method is illustrated on a set of simple regression data and a set of multiple regression data. The format of the chapter, with description, justification, and illustration separated into subsections and with many details deferred to notes at the end of the chapter, is intended to allow flexibility of use. Depending on the reader’s-current purpose, he or she could pick out the material relating to either the “how” or the “why” of the method. The notes provide additional material for a second or third reading. The descriptions and illustrations are given in sufficient detail to enable the reader to write computer programs implementing the methods. Near the end of each chapter, a section on computation includes a small test case for debugging such a program and also mentions existing programs and packages. Anyone who is interested in regression should learn about the alternatives to least squares. This book could serve as a textbook for a course following a least-squares regression course or for self-study. It is also a good reference for practitioners who may want to supplement a least-squares regression analysis with an alternative analysis.
From the Back Cover
Of related interest Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ” an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models highly recommend[ed] for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models.” Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ” [a] comprehensive and scholarly work impressively thorough with attention given to every aspect of the modeling process ” Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models growth, compartmental, and multiphase and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge “The authors have achieved their stated intention in an outstanding and useful manner for both students and researchers Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes Should be in the hands of all system analysts and computer system architects.” Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
About the Author
About the authors DAVID BIRKES is an Associate Professor in the Department of Statistics at Oregon State University. He received his PhD in mathematics from the University of Washington. YADOLAH DODGE is Professor of Statistics and Operations Research at the University of Neuchatel, Switzerland. The author of Mathematical Programming in Statistics and Analysis of Experiments with Missing Data, Dr. Dodge obtained his PhD in statistics from Oregon State University and is an elected member of the International Statistical Institute.
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