Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples - Springer Series in Statistics (Hardback)
  • Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples - Springer Series in Statistics (Hardback)
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Statistical Tools for Nonlinear Regression: A Practical Guide With S-PLUS and R Examples - Springer Series in Statistics (Hardback)

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Hardback 234 Pages / Published: 12/09/2003
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Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.

Publisher: Springer-Verlag New York Inc.
ISBN: 9780387400815
Number of pages: 234
Weight: 1170 g
Dimensions: 234 x 152 x 15 mm
Edition: 2nd ed. 2004


MEDIA REVIEWS

From the reviews of the second edition:

"Users of S-PLUS or R who do nonlinear estimation would certainly want a copy of this book. The wealth of applications and code for using the specialized software transcends the limitation of the applications to medicine and biology." Technometrics, May 2004

"In this second edition to the first edition published in 1996, the authors present a comprehensive overview of nonlinear regression methods. With an emphasis on learning the basics of how to perform analyses using S-PLUS or R and understand and present the results, the book provides a valuable resource for those interested in learning this material...The book is easy to read, and the inclusion of S-PLUS output, graphs, and source code makes picking up the book and getting started much easier. For those working with data best modeled by nonlinear relationships, this book will be a valuable addition to your shelf of resources." Journal of the American Statistical Association, September 2004

"As the title suggests, the book deals with non-linear regression analysis ... . The real strength of the book lies in a careful and detailed discussion of a number of examples ... . Anyone who is interested in actually analysing data using non-linear models will benefit from working through these examples ... . the book would make an excellent secondary source for a course in non-linear models. ... A number of excellent references are available that provide the necessary theoretical background ... ." (Christopher Cox, Statistics in Medicine, Vol. 24 (13), 2005)

"This second edition provides a comprehensive overview of the field of parametric nonlinear regression models in data analysis. The book aims especially at students, as a tutorial book, and at the scientists applying statistical methods in different practical domains. Each chapter begins with a set of different concrete examples, followed by the corresponding statistical issues and solutions. In addition, where necessary, a very simple theoretical background is provided." (Florin Gorunescu, Zentralblatt MATH, Vol. 1041 (16), 2004)

"The first 5 chapters of this book discuss normal distribution models where the mean is described with a nonlinear model. Chapter six discusses a nonlinear model with a binomial distribution, chapter seven uses a Poisson and multinomial distribution. ... The large amount of examples ... makes this book a valuable contribution to the every day statistical practice." (J. van den Broek, Kwantitatieve Methoden, Issue 72B34, 2004)

"This book describes itself as a `cookbook' for non-linear regression and is supported by the nls2 software ... . The chapters are reasonably and logically laid out ... . There are 42 references, many of which are to other text-books on modeling ... . The back cover suggests that it may be of use to students as a tutorial book. It is certainly a valuable complement to the nls2 software ... ." (Paul Hewson, Journal of the Royal Statistical Society, Vol. 198 (1), 2005)

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