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Ill-conditioned information matrices and the generalized linear model: an asymptotically biased estimation approach

In the regression framework of the generalized linear model (Nelder and Wedderburn (1972)), interative maximum likelihood parameter estimation is employed via the method of scoring. This iterative procedure involves a key matrix, the information matrix. Ill-conditioning of the information matrix can be responsible for making many desirable properties of the parameter estimates unattainable. Some asymptotically biased alternatives to maximum likelihood estimation are put forth which alleviate the detrimental effects of near singular information. Notions of ridge estimation (Hoerl and Kennard (1970a) and Schaefer (1979)), principal component estimation (Webster et al. (1974) and Schaefer (1986)), and Stein estimation (Stein (1960)) are extended into a regression setting utilizing any one of an entire class of response distributions. / Ph. D.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/53584
Date January 1988
CreatorsMarx, Brian D.
ContributorsStatistics, Myers, Raymond, Birch, Jeffrey B., Terrell, George R., Brooks, Camilla A., Smith, Eric P., Hinkelmann, Klaus
PublisherVirginia Polytechnic Institute and State University
Source SetsVirginia Tech Theses and Dissertation
Languageen_US
Detected LanguageEnglish
TypeDissertation, Text
Formatxi, 178 leaves, application/pdf, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/
RelationOCLC# 18668924

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