Robust modeling for inference from generalized linear model classes
Robust modeling for inference from generalized linear model classes
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DOI：
10.1198/016214507000000518
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发表时间：
2007-09-01
影响因子：
3.7
通讯作者：
Lee, Youngjo
中科院分区：
文献类型：
--
作者：
Noh, Maengseok;Lee, Youngjo
广义线性模型（GLM）广泛用于数据分析；然而，它们的最大似然估计可能对异常值很敏感。我们提出了新的统计模型，允许从 GLM 类模型（包括泊松和二项式 GLM）以及它们对广义线性混合模型的扩展中进行稳健的推论。新模型的似然得分方程为估计量提供了有限的影响，因此所得的估计量对于异常值具有鲁棒性，同时在没有异常值的情况下保持高效率。
Generalized linear models (GLMs) are widely used for data analysis; however, their maximum likelihood estimators can be sensitive to outliers. We propose new statistical models that allow robust inferences from the GLM class of models, including Poisson and binomial GLMs, and their extension to generalized linear mixed models. The likelihood score equations from the new models give estimators with bounded influence, so that the resulting estimators are robust against outliers while maintaining high efficiency in the absence of outliers.