Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-26T02:49:08.813Z Has data issue: false hasContentIssue false

An algorithm for exponential fitting revisited

Published online by Cambridge University Press:  14 July 2016

Abstract

An algorithm for exponential fitting is presented which exploits the separable regression structure and a reparametrization. The algorithm has proved very satisfactory, and theoretical reasons for this are developed.

Type
Part 7—Algorithms and Computations
Copyright
Copyright © 1986 Applied Probability Trust 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Golub, G. H. and Pereyra, V. (1973) The differentiation of pseudo-inverses and nonlinear least squares problems whose variables separate. SIAM J. Numer. Anal. 10, 413432.Google Scholar
Jennrich, R. I. (1969) Asymptotic properties of non-linear least squares estimators. Ann. Math. Statist. 40, 633643.CrossRefGoogle Scholar
Osborne, M. R. (1975) Some special nonlinear least squares problems. SIAM J. Numer. Anal. 12, 571592.CrossRefGoogle Scholar
Osborne, M. R. (1976) Nonlinear least squares–the Levenberg algorithm revisited J. Austral. Math. Soc. B 19, 343357.CrossRefGoogle Scholar
Richards, F. S. G. (1961) A method of maximum-likelihood estimation. J. R. Statist. Soc. B 23, 469475.Google Scholar
Smyth, G. K. (1985) Separable Parameters and Coupled Iterations in Nonlinear Estimation, Ph.D. Thesis, Australian National University, Canberra.Google Scholar
Wilson, S. R. (1983) Benchmark data sets for the flexible evaluation of statistical software. Computational Statistics and Data Analysis 1, 2939.Google Scholar