Customer Reviews

Nonlinear Regression (Wiley Series in Probability and Statistics)

5 star:		(3)
4 star:		(1)
3 star:		(0)
2 star:		(0)
1 star:		(0)

 

 

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.

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

This book covers the whole theory of nonlinear regression. I think it is essential both for students of statistics and for scientists, not only as a study book but also as a reference book. I recommend it to those who already have had an introductory course on the subject and need to go deeper into it.

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.