Nonlinear Regression (Wiley Series in Probability and Statistics)

Seber, George A. F.; Wild, C. J.

ISBN 10: 0471617601 / ISBN 13: 9780471617600

Published by Wiley, 1989

About this Item

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Seller Inventory # OTV.0471617601.G

Bibliographic Details

• Title: Nonlinear Regression (Wiley Series in Probability and Statistics)
• Publisher: Wiley
• Publication Date: 1989
• Language: English
• ISBN 10: 0471617601
• ISBN 13: 9780471617600
• Binding: Hardcover
• Condition: good

About this title

Synopsis

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

From the Publisher

This text/reference provides a broad survey of aspects of model-building and statistical inference. Presents an accessible synthesis of current theoretical literature, requiring only familiarity with linear regression methods. The three chapters on central computational questions comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.