You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
The Unifying Role of Iterative Generalized Least Squares in Statistical Algorithms
Guido del Pino
Vol. 4, No. 4 (Nov., 1989), pp. 394-403
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2245853
Page Count: 10
You can always find the topics here!Topics: Least squares, Mathematical problems, Mathematical functions, Maximum likelihood estimation, Statism, Estimators, Generalized linear model, Matrices, Statistical models, Statistics
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
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.
Statistical Science © 1989 Institute of Mathematical Statistics