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Separable least squares, variable projection and the Gauss-Newton algorithm

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Date

2007

Authors

Osborne, Michael

Journal Title

Journal ISSN

Volume Title

Publisher

Kent State University

Abstract

A regression problem is separable if the model can be represented as a linear combination of functions which have a nonlinear parametric dependence. The Gauss-Newton algorithm is a method for minimizing the residual sum of squares in such problems. It is

Description

Keywords

Keywords: Algorithms; Boolean functions; Convergence of numerical methods; Curve fitting; Errors; Maximum likelihood estimation; Measurement errors; Mobile telecommunication systems; Newton-Raphson method; Random errors; Consistency; Expected Hessian; Kaufman's mod Consistency; Expected Hessian; Kaufman's modification; Large data sets; Law of large numbers; Maximum likelihood; Newton's method; Nonlinear least; Random errors; Rate of convergence; Scoring; Squares

Citation

URI

http://hdl.handle.net/1885/36072

Collections

ANU Research Publications

Source

Electronic Transactions on Numerical Analysis

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31

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01_Osborne_Separable_least_squares,_2007.pdf (307.06 KB)

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