Robust modeling for inference from generalized linear model classes

• 成果类型: Article
• 署名作者: Noh, Maengseok; Lee, Youngjo
• 署名单位: Pukyong National University; Seoul National University (SNU)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000000518
• 发表日期: 2007
• 页码: 1059-1072
• 关键词: maximum-likelihood

摘要

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.