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Titlebook: GLIM 82: Proceedings of the International Conference on Generalised Linear Models; Proceedings of the I Robert Gilchrist Conference proceed

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樓主: 使委屈
51#
發(fā)表于 2025-3-30 08:46:34 | 只看該作者
A GLM FOR Estimating Probabilities in Retrospective Case-Control Studies,A simple GLM for estimating probabilities of disease incidence across subgroups of a population from retrospective case-control studies is proposed. The method is illustrated by an application of GLIM to a cross classification table from a study of cot deaths in Lambeth.
52#
發(fā)表于 2025-3-30 13:27:58 | 只看該作者
53#
發(fā)表于 2025-3-30 19:38:48 | 只看該作者
54#
發(fā)表于 2025-3-30 21:22:39 | 只看該作者
A Case-Based Approach to Knee Painlems for probability models in genetics. It is shown how GLIM-3 may be used to obtain estimates of gene frequencies, inbreeding coefficients and other parameters and their asymptotic covari-ances and standard errors. Examples are taken from medical genetics and evolutionary biology.
55#
發(fā)表于 2025-3-31 01:01:41 | 只看該作者
56#
發(fā)表于 2025-3-31 05:47:16 | 只看該作者
A Model for a Binary Response with Misclassifications,intly with the error probabilities for the two types of misclassification. The model is illustrated using a subset of the well-known coal-miners data. The problem is formulated as an incomplete data problem and the EM-algorithm and GLIM is used for estimation. The connection to latent structure models is discussed.
57#
發(fā)表于 2025-3-31 09:30:53 | 只看該作者
Loglinear Models with Composite Link Functions in Genetics,lems for probability models in genetics. It is shown how GLIM-3 may be used to obtain estimates of gene frequencies, inbreeding coefficients and other parameters and their asymptotic covari-ances and standard errors. Examples are taken from medical genetics and evolutionary biology.
58#
發(fā)表于 2025-3-31 16:55:48 | 只看該作者
Conference proceedings 1982odels. Later sections of the volume cover other invited and contributed papers on the theory and application of generalised linear models. Included amongst these is a paper by Murray Aitkin, proposing a unified approach to statistical modelling through direct likelihood inference, and a paper by Dar
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