复杂调查数据的线性混合模型：实施和评估成对可能性

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Stat Pub Date : 2024-02-27 DOI:10.1002/sta4.657

Thomas Lumley, Xudong Huang

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引用次数: 0

摘要

随着复杂的调查数据越来越广泛地应用于健康和社会科学研究，人们对拟合更广泛的回归模型越来越感兴趣。我们介绍了使用 Rao 及其合作者的成对复合似然法在 R 中实现两级线性混合模型的方法。我们讨论了成对复合似然的计算效率，并在模拟和国际学生评估项目（PISA）教育调查数据中将该估计器与现有的顺序伪似然估计器进行了比较。

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Linear mixed models for complex survey data: Implementing and evaluating pairwise likelihood

As complex-survey data become more widely used in health and social science research, there is increasing interest in fitting a wider range of regression models. We describe an implementation of two-level linear mixed models in R using the pairwise composite likelihood approach of Rao and co-workers. We discuss the computational efficiency of pairwise composite likelihood and compare the estimator to the existing sequential pseudolikelihood estimator in simulations and in data from the Programme for International Student Assessment (PISA) educational survey.

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来源期刊

Stat Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.10

自引率

0.00%

发文量

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