期刊
BIOMETRIKA
卷 91, 期 2, 页码 447-459出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/91.2.447
关键词
data contamination; generalised method of moments; influence function; longitudinal data; M-estimator; outlier; redescending property
资金
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0902232] Funding Source: National Science Foundation
In the presence of data contamination or outliers, some empirical studies have indicated that the two methods of generalised estimating equations and quadratic inference functions appear to have rather different robustness behaviour. This paper presents a theoretical investigation from the perspective of the influence function to identify the causes for the difference. We show that quadratic inference functions lead to bounded influence functions and the corresponding M-estimator has a redescending property, but the generalised estimating equation approach does not. We also illustrate that, unlike generalised estimating equations, quadratic inference functions can still provide consistent estimators even if part of the data is contaminated. We conclude that the quadratic inference function is a preferable method to the generalised estimating equation as far as robustness is concerned. This conclusion is supported by simulations and real-data examples.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据