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本文引用的文献

1
Fast Pure R Implementation of GEE: Application of the Matrix Package.广义估计方程（GEE）的快速纯R实现：矩阵包的应用。
R J. 2013 Jun;5(1):181-187.
2
Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes.具有二元结局的整群随机试验的偏差校正三明治估计量的小样本性能。
Stat Med. 2015 Jan 30;34(2):281-96. doi: 10.1002/sim.6344. Epub 2014 Oct 24.
3
Assessing effects of cholera vaccination in the presence of interference.在存在干扰的情况下评估霍乱疫苗的效果。
Biometrics. 2014 Sep;70(3):731-44. doi: 10.1111/biom.12184. Epub 2014 May 20.
4
Small sample GEE estimation of regression parameters for longitudinal data.纵向数据中回归参数的小样本 GEE 估计。
Stat Med. 2014 Sep 28;33(22):3869-81. doi: 10.1002/sim.6198. Epub 2014 May 4.
5
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.在因果治疗效果估计中通过倾向得分进行分层和加权：一项比较研究。
Stat Med. 2004 Oct 15;23(19):2937-60. doi: 10.1002/sim.1903.
6
Small-sample adjustments for Wald-type tests using sandwich estimators.使用三明治估计量对 Wald 型检验进行小样本调整。
Biometrics. 2001 Dec;57(4):1198-206. doi: 10.1111/j.0006-341x.2001.01198.x.

使用geex在R中进行M估计的计算

The Calculus of M-Estimation in R with geex.

作者信息

Saul Bradley C, Hudgens Michael G

机构信息

NoviSci, LLC.

UNC Chapel Hill.

出版信息

J Stat Softw. 2020 Feb;92(2). doi: 10.18637/jss.v092.i02. Epub 2020 Feb 18.

DOI:10.18637/jss.v092.i02
PMID:33859545
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8046178/
Abstract

M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set of user-specified, unbiased estimating equations. Examples from the M-estimation primer by Stefanski and Boos (2002) demonstrate use of the software. The package also includes a framework for finite sample, heteroscedastic, and autocorrelation variance corrections, and a website with an extensive collection of tutorials.

摘要

M估计法，即估计方程法，广泛适用于点估计和渐近推断。在本文中，我们展示了一个R软件包，它可以找到根，并为任何一组用户指定的无偏估计方程计算经验三明治方差估计量。Stefanski和Boos（2002）所著的《M估计入门》中的例子展示了该软件的用法。该软件包还包括一个用于有限样本、异方差和自相关方差校正的框架，以及一个拥有大量教程的网站。