Generalized Linear Latent and Mixed Models with Composite Links and Exploded Likelihoods

机译：具有链接和爆炸可能性的广义线性潜在模型和混合模型

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摘要

Applications of composite links and exploded likelihoods for generalized linear latent and mixed models are explored.

机译：探索了组合链接和爆炸可能性在广义线性潜伏和混合模型中的应用。

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来源
《International Workshop on Statistical Modelling; 20040704-08; Florence(IT)》|2004年|P.27-39|共13页
会议地点 Florence(IT)
作者
Anders Skrondal; Sophia Rabe-Hesketh;
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作者单位

Norwegian Institute of Public Health;

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会议组织
原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
generalized linear latent and mixed models; composite link; exploded likelihood;

机译：广义线性潜在和混合模型;复合链接;爆炸似然;

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专利

1. Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods [J] . Sophia Rabe-Hesketh, Anders Skrondal Psychometrika . 2007,第2期

机译：具有组合链接和爆炸可能性的多级潜在变量建模
2. Multilevel and latent variable modeling with composite links and exploded likelihoods [J] . Rabe-Hesketh S, Skrondal A Psychometrika . 2007,第2期

机译：具有组合链接和爆炸可能性的多级潜变量建模
3. Approximate likelihood inference in spatial generalized linear mixed models with closed skew normal latent variables [J] . Communications in Statistics . 2020,第1a2期

机译：具有闭合偏正态潜在变量的空间广义线性混合模型的近似似然推断
4. Generalized Linear Latent and Mixed Models with Composite Links and Exploded Likelihoods [C] . Anders Skrondal, Sophia Rabe-Hesketh International Workshop on Statistical Modelling . 2004

机译：具有复合链路和爆炸性似然的广义线性潜在和混合模型
5. Monte Carlo Likelihood Approximation for Generalized Linear Mixed Models. [D] . Knudson, Christina. 2016

机译：广义线性混合模型的蒙特卡洛似然近似。
6. High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets [O] . Cristian G. Bologa, Vernon Shane Pankratz, Mark L. Unruh, 2021

机译：广义线性混合模型的分层可能性高性能实施：估计大规模电子健康记录数据集中钾参考范围的应用
7. Multilevel and latent variable modeling with composite links and exploded likelihoods [O] . Rabe-Hesketh, Sophia, Skrondal, Anders 2007

机译：具有复合链接和爆炸可能性的多级和潜在变量建模

Generalized Linear Latent and Mixed Models with Composite Links and Exploded Likelihoods

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