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August 2006 Design Issues for Generalized Linear Models: A Review
André I. Khuri, Bhramar Mukherjee, Bikas K. Sinha, Malay Ghosh
Statist. Sci. 21(3): 376-399 (August 2006). DOI: 10.1214/088342306000000105


Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated. The choice of design for a GLM is a very important task in the development and building of an adequate model. However, one major problem that handicaps the construction of a GLM design is its dependence on the unknown parameters of the fitted model. Several approaches have been proposed in the past 25 years to solve this problem. These approaches, however, have provided only partial solutions that apply in only some special cases, and the problem, in general, remains largely unresolved. The purpose of this article is to focus attention on the aforementioned dependence problem. We provide a survey of various existing techniques dealing with the dependence problem. This survey includes discussions concerning locally optimal designs, sequential designs, Bayesian designs and the quantile dispersion graph approach for comparing designs for GLMs.


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André I. Khuri. Bhramar Mukherjee. Bikas K. Sinha. Malay Ghosh. "Design Issues for Generalized Linear Models: A Review." Statist. Sci. 21 (3) 376 - 399, August 2006.


Published: August 2006
First available in Project Euclid: 20 December 2006

zbMATH: 1246.62168
MathSciNet: MR2339137
Digital Object Identifier: 10.1214/088342306000000105

Rights: Copyright © 2006 Institute of Mathematical Statistics


Vol.21 • No. 3 • August 2006
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