Generalized Linear Models with Random Effects 论文

2006引用 321
DOI
Bayesian Modeling and Causal InferenceBayesian Methods and Mixture ModelsNeural Networks and Applications
人工智能Neural Networks and ApplicationsBayesian Methods and Mixture ModelsBayesian Modeling and Causal Inference
相关技术:Bayesian Methods and Mixture ModelsNeural Networks and ApplicationsBayesian Modeling and Causal Inference
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摘要

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining informati

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Y

Yudi Pawitan

J

J. A. Nelder

Y

Youngjo Lee

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Bayesian Methods and Mixture ModelsNeural Networks and ApplicationsBayesian Modeling and Causal Inference

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