A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations.

IF 1.8 4区数学 Q1 STATISTICS & PROBABILITY

American Statistician Pub Date : 2017-01-01 Epub Date: 2018-01-11 DOI:10.1080/00031305.2016.1200490

Philip M Westgate, Woodrow W Burchett

引用次数: 17

Abstract

ABSTRACT Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Therefore, correlation structure selection criteria should penalize, or account for, correlation parameter estimation. In this article, we compare recently proposed penalties in terms of their impacts on correlation structure selection and regression parameter estimation, and give practical considerations for data analysts. Supplementary materials for this article are available online.

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广义估计方程的相关结构选择惩罚比较。

相关数据通常使用由总体平均广义估计方程(GEEs)构建的模型进行分析。总体平均的GEE模型的规范包括选择描述重复测量的相关性的结构。该结构的准确规范可以提高效率，而有害相关参数的有限样本估计会使回归参数估计的方差增大。因此，相关结构选择标准应该惩罚或考虑相关参数估计。在本文中，我们比较了最近提出的惩罚对相关结构选择和回归参数估计的影响，并为数据分析人员提供了实际考虑。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

American Statistician 数学-统计学与概率论

CiteScore

3.50

自引率

5.60%

发文量

审稿时长

>12 weeks

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