Ebook Download Nonlinear Regression, by George A. F. Seber, C. J. Wild
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Nonlinear Regression, by George A. F. Seber, C. J. Wild
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WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
From the Reviews of Nonlinear Regression
"A very good book and an important one in that it is likely to become a standard reference for all interested in nonlinear regression; and I would imagine that any statistician concerned with nonlinear regression would want a copy on his shelves."
–The Statistician
"Nonlinear Regression also includes a reference list of over 700 entries. The compilation of this material and cross-referencing of it is one of the most valuable aspects of the book. Nonlinear Regression can provide the researcher unfamiliar with a particular specialty area of nonlinear regression an introduction to that area of nonlinear regression and access to the appropriate references . . . Nonlinear Regression provides by far the broadest discussion of nonlinear regression models currently available and will be a valuable addition to the library of anyone interested in understanding and using such models including the statistical researcher."
–Mathematical Reviews
- Sales Rank: #1212438 in Books
- Published on: 2003-09-19
- Released on: 2003-09-05
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.60" w x 6.20" l, 2.44 pounds
- Binding: Paperback
- 792 pages
Most helpful customer reviews
27 of 27 people found the following review helpful.
one of three excellent books on nonlinear regression
By Michael R. Chernick
In 2001 I reviewed for amazon the texts by Gallant and the one by Bates and Watts. This text was written by Seber and Wild, two accomplished statisticians and experienced authors. This volume is of the same high caliber as those texts and deserves mention. It is a longer text that overlaps on many topics with the other two books, deliberately neglects some areas that were well covered by Gallant (Gallant's book came out in 1987 and this one in 1989) and hits some topics not covered by either of the other two books.
Bootstrap methods are neglected probably because the value of the bootstrap for standard error estimation in nonlinear models was not yet appreciated in 1989.
Chapters 1 and 2 provide good introductory material similar to the other texts. Chapter 1 deals with the models (linear and nonlinear) and Chapter 2 provides the basic estimation techniques. In addition to the standard material on least squares, generalized least squares and maximum likelihood, the authors also cover quasi-likelihood, linear approximations, robust estimation and Bayesian methods. Box - Cox transformations and the issue of variance heterogeneity are also treated in Chapter 2.
As they remark in the preface, they avoid much of the econometric theory and asymptotic theory that is well covered in Gallant's book.
Chapter 3 deals with important practical issues including the convergence properties of the iterative procedures (important for nonlinear models but a non-issue in linear models), ill-conditioning and identifiability (important issues for both linear and nonlinear models).
Chapter 4 deals with curvature issues and covers much of the original work of Bates and Watts with many references to those authors. Oddly though, there is no mention of the Bates and Watts text. Both books were published by Wiley around the same time with Bates and Watts appearing in 1988 and Seber and Wild in 1989. Perhaps the Seber and Wild book went to the publisher before the Bates and Watts book came out (their preface has a May 1988 date).
Important and interesting topics covered in this book but not the others include models with time dependent errors, detailed treatment of growth models, compartmental models, multiphase and spline regresions and error-in-variables models. They also devote a whole chapter to software issues (very interesting and practical but probably mostly outdated).
Good for a graduate statistics course or for a research reference source. Has lots of material and references but lacks homework problems.
6 of 6 people found the following review helpful.
Good Text: Rigorous and Theoretical but Clear
By Marco Respini
I am a Chemist Phd working in the oil refining industry and often use statistical tools to model plant process and laboratory data. this text is not for practical uses but is to be considered a very rigorous and in depht introduction to nonlinear regression theory. Even if quite long and detailed is still clear and not difficult to understand. if you have time to devote to the principles and not jump to solutions this is a very good advanced text
4 of 4 people found the following review helpful.
It is a bible of nonlinear regression book
By C. Tu
This is a very good book for people who would like to learn nonlinear regression in deep. Comparing with Bates and Watts book, this book provides very clear nonlinear regression theories. For all statisticians who focus on nonlinear regression, they must have this book.
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