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November, 1989 The Unifying Role of Iterative Generalized Least Squares in Statistical Algorithms
Guido del Pino
Statist. Sci. 4(4): 394-403 (November, 1989). DOI: 10.1214/ss/1177012408

Abstract

This expository paper deals with the role of iterative generalized least squares as an algorithm for the computation of statistical estimators. Relationships between various algorithms, such as Newton-Raphson, Gauss-Newton, and scoring, are studied. A parallel is made between statistical properties of the model and the structure of the numerical algorithm employed to find parameter estimates. In particular a general linearizability property that extends the concept of link function in generalized linear models is considered and its computational meaning is discussed. Maximum quasilikelihood estimators are reinterpreted so that they may exist even when there is no quasilikelihood function.

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Guido del Pino. "The Unifying Role of Iterative Generalized Least Squares in Statistical Algorithms." Statist. Sci. 4 (4) 394 - 403, November, 1989. https://doi.org/10.1214/ss/1177012408

Information

Published: November, 1989
First available in Project Euclid: 19 April 2007

zbMATH: 0955.62607
MathSciNet: MR1041764
Digital Object Identifier: 10.1214/ss/1177012408

Rights: Copyright © 1989 Institute of Mathematical Statistics

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Vol.4 • No. 4 • November, 1989
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