Alternative Methods of Regression. Wiley Series in Probability and Statistics

  • ID: 2175701
  • Book
  • 240 Pages
  • John Wiley and Sons Ltd
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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.
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Linear Regression Analysis.

Constructing and Checking the Model.

Least Squares Regression.

Least Absolute Deviations Regression.


Nonparametric Regression.

Bayesian Regression.

Ridge Regression.


Other Methods.
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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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