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Robust estimation of models for longitudinal data with dropouts and outliers
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2020-11-10 , DOI: 10.1080/02664763.2020.1845623
Yuexia Zhang 1 , Guoyou Qin 2 , Zhongyi Zhu 3 , Bo Fu 4
Affiliation  

ABSTRACT

Missing data and outliers usually arise in longitudinal studies. Ignoring the effects of missing data and outliers will make the classical generalized estimating equation approach invalid. The longitudinal cohort study of rheumatoid arthritis patients was designed to investigate whether the Health Assessment Questionnaire score was associated with baseline covariates and changed with time. There exist dropouts and outliers in the data. In order to analyze the data, we develop a robust estimating equation approach. To deal with the responses missing at random, we extend a doubly robust method. To achieve robustness against outliers, we utilize an outlier robust method, which corrects the bias induced by outliers through centralizing the covariate matrix in the estimating equation. The doubly robust method for dropouts is easy to combine with the outlier robust method. The proposed method has the property of robustness in the sense that the proposed estimator is not only doubly robust against model misspecification for dropouts when there is no outlier in the data, but also robust against outliers. Consistency and asymptotic normality of the proposed estimator are established under regularity conditions. A comprehensive simulation study and real data analysis demonstrate that the proposed estimator does have the property of robustness.



中文翻译:

具有丢失和异常值的纵向数据模型的稳健估计

摘要

缺失数据和异常值通常出现在纵向研究中。忽略缺失数据和异常值的影响将使经典的广义估计方程方法无效。类风湿性关节炎患者的纵向队列研究旨在调查健康评估问卷评分是否与基线协变量相关并随时间变化。数据中存在丢失和异常值。为了分析数据,我们开发了一种稳健的估计方程方法。为了处理随机缺失的响应,我们扩展了一种双重鲁棒的方法。为了实现对异常值的鲁棒性,我们使用了一种异常值鲁棒方法,该方法通过在估计方程中集中协变量矩阵来纠正异常值引起的偏差。用于 dropouts 的双重鲁棒方法很容易与异常值鲁棒方法相结合。所提出的方法具有鲁棒性,因为所提出的估计器不仅在数据中没有异常值时对模型错误指定具有双重鲁棒性,而且对于异常值也具有鲁棒性。所提出的估计量的一致性和渐近正态性是在正则条件下建立的。综合仿真研究和真实数据分析表明,所提出的估计器确实具有鲁棒性。所提出的估计量的一致性和渐近正态性是在正则条件下建立的。综合仿真研究和真实数据分析表明,所提出的估计器确实具有鲁棒性。所提出的估计量的一致性和渐近正态性是在正则条件下建立的。综合仿真研究和真实数据分析表明,所提出的估计器确实具有鲁棒性。

更新日期:2020-11-10
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