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.
General
Imprint: |
Springer-Verlag New York
|
Country of origin: |
United States |
Series: |
Springer Series in Statistics |
Release date: |
October 2011 |
First published: |
1990 |
Authors: |
Gavin J. S Ross
|
Dimensions: |
235 x 155 x 10mm (L x W x T) |
Format: |
Paperback
|
Pages: |
189 |
Edition: |
Softcover reprint of the original 1st ed. 1990 |
ISBN-13: |
978-1-4612-8001-9 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Applied mathematics >
General
|
LSN: |
1-4612-8001-X |
Barcode: |
9781461280019 |
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