Nonlinear Estimation

Gavin J. S. Ross

ISBN 10: 146128001X / ISBN 13: 9781461280019

Published by Springer, Springer Okt 2011, 2011

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer. A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier comparison of rival models. The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets. The book combines an algebraic, a geometric and a computational approach, and is illustrated with practical examples. A final chapter shows how this approach is implemented in the author's Maximum Likelihood Program, MLP. 200 pp. Englisch.

Seller Inventory # 9781461280019

Bibliographic Details

• Title: Nonlinear Estimation
• Publisher: Springer, Springer Okt 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 146128001X
• ISBN 13: 9781461280019
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 312 grams

About this title

Synopsis

Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer. A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier comparison of rival models. The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets. The book combines an algebraic, a geometric and a computational approach, and is illustrated with practical examples. A final chapter shows how this approach is implemented in the author's Maximum Likelihood Program, MLP.

Review

"The novel part of this book is a central theme on the important differences in specific parts of the problem... It contains many interesting ideas, particularly with regard to parameterizations and graphics... people with the relevant type of data should find careful study of this book very rewarding."
(Journal of the Am. Statistical Assoc.)