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• W3100822788 abstract "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." @default.
• W3100822788 created "2020-11-23" @default.
• W3100822788 creator A5034876222 @default.
• W3100822788 creator A5047432897 @default.
• W3100822788 creator A5067400593 @default.
• W3100822788 creator A5082251592 @default.
• W3100822788 date "2006-08-01" @default.
• W3100822788 modified "2023-10-02" @default.
• W3100822788 title "Design Issues for Generalized Linear Models: A Review" @default.
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