Nonlinear Regression Analysis and Its Applications. Wiley Series in Probability and Statistics

  • ID: 2173739
  • Book
  • 366 Pages
  • John Wiley and Sons Ltd
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Wiley–Interscience Paperback Series

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"The authors have put together 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."

"[This book] provides a good balance of relevant theory and application with many examples . . . [and it] provides the most balanced approach to theory and application appropriate for a first course in nonlinear regression modeling for graduate statistics students."Mathematical Reviews

"[This book] joins a distinguished list of publications with a reputation for balancing technical rigor with readability, and theory with application. [It] upholds tradition . . . [and is] a worthwhile reference for the marketing researcher with a serious interest in linear models."Journal of Marketing Research

This book offers a balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression and provides background material on linear regression, including the geometrical development for linear and nonlinear least squares. The authors employ real data sets throughout, and their extensive use of geometric constructs and continuing examples makes the progression of ideas appear very natural. The book also includes pseudocode for computing algorithms.

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Review of Linear Regression.

Nonlinear Regression: Iterative Estimation and Linear Approximations.

Practical Considerations in Nonlinear Regression.

Multiresponse Parameter Estimation.

Models Defined by Systems of Differential Equations.

Graphical Summaries of Nonlinear Inference Regions.

Curvature Measures of Nonlinearity.

Appendix 1: Data Sets Used in Examples.

Appendix 2: QR Decompositions Using Householder Transformations.

Appendix 3: Pseudocode for Computing Algorithms.

Appendix 4: Data Sets Used in Problems.

Appendix 5: Evaluating Matrix Exponentials and Convolutions.

Appendix 6: Interpolating Profile Pair Contours.

Appendix 7: Key to Data Sets.




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Douglas M. Bates, PhD, is Professor of Statistics at the University of Wisconsin Madison. Donald G. Watts, PhD, is Professor Emeritus in Statistics at Queens University in Ontario, Canada.

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