Nonlinear Estimation

  • Gavin J. S. Ross

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Gavin J. S. Ross
    Pages 1-11
  3. Gavin J. S. Ross
    Pages 12-43
  4. Gavin J. S. Ross
    Pages 44-72
  5. Gavin J. S. Ross
    Pages 73-107
  6. Gavin J. S. Ross
    Pages 108-133
  7. Gavin J. S. Ross
    Pages 134-142
  8. Gavin J. S. Ross
    Pages 143-173
  9. Back Matter
    Pages 174-189

About this book


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.


Curve fitting Fitting Likelihood Variance algorithms linear optimization optimization

Authors and affiliations

  • Gavin J. S. Ross
    • 1
  1. 1.Statistics DepartmentInstitute of Arable Crops Research Rothamsted Experimental StationHarpenden, HertsEngland

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1990
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8001-9
  • Online ISBN 978-1-4612-3412-8
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site