Modifications of REML algorithm for HGLMs

Woojoo Lee; Youngjo Lee

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Modifications of REML algorithm for HGLMs

机译：HGLM REML算法的修改

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摘要

Hierarchical generalized linear models (HGLMs) have become popular in data analysis. However, their maximum likelihood (ML) and restricted maximum likelihood (REML) estimators are often difficult to compute, especially when the random effects are correlated; this is because obtaining the likelihood function involves high-dimensional integration. Recently, an h-likelihood method that does not involve numerical integration has been proposed. In this study, we show how an h-likelihood method can be implemented by modifying the existing ML and REML procedures. A small simulation study is carried out to investigate the performances of the proposed methods for HGLMs with correlated random effects.

机译：分层广义线性模型（HGLM）在数据分析中变得很流行。但是，它们的最大似然（ML）和受限最大似然（REML）估计量通常难以计算，尤其是在随机效应相关的情况下；这是因为获得似然函数涉及高维积分。最近，已经提出了一种不涉及数值积分的h-似然法。在这项研究中，我们展示了如何通过修改现有的ML和REML程序来实现h似然法。进行了一个小型模拟研究，以研究所提出的具有相关随机效应的HGLM方法的性能。

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来源
《Statistics and computing》 |2012年第4期|p.959-966|共8页
作者
Woojoo Lee; Youngjo Lee;
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作者单位

Department of Medical Epidemiology and Biostatistics,Karolinska Institutet, 17177, Stockholm, Sweden,Department of Statistics, Inha University, 253 Yonghyun-dong,Nam-gu, Incheon, 402-751, Korea;

Department of Statistics, Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul, 151-742, Korea;

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正文语种 eng
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关键词
hierarchical generalized linear model; hierarchical likelihood; penalized quasi-likelihood; restricted likelihood; variance components;

机译：分层广义线性模型等级似然罚准似然;限制可能性;方差成分;

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Modifications of REML algorithm for HGLMs

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