Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data
Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data
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DOI：
10.1080/00949655.2022.2055758
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发表时间：
2022-03-26
影响因子：
1.2
通讯作者：
Zhang，Qingzhao
中科院分区：
文献类型：
--
作者：
Fang，Kuangnan;Fan，Xinyan;Zhang，Qingzhao
纵向数据在许多领域中是经常遇到的。人们已经提出了许多统计模型，其中时变系数模型已被证明是有效的许多实际问题。长尾/污染分布并不罕见，不能使用非稳健的基于似然的估计来适应。许多现有方法的另一个共同局限性是没有充分考虑协变量之间的相互关系。在本研究中，我们采用最小绝对偏差损失函数来达到稳健性。对于相关协变量的选择，采用惩罚方法。显着推进从现有的文献中，我们描述了协变量之间的互连使用网络结构，并开发新的惩罚，以适应网络的连接和连接措施。一致性属性是严格建立的。数值研究，包括模拟和数据分析，证明了所提出的方法的竞争力的实际性能。
Longitudinal data are commonly encountered in many fields. Many statistical models have been developed, among which the time-varying coefficient model has been shown to be effective for many practical problems. Long-tailed/contaminated distributions are not uncommon and cannot be accommodated using non-robust likelihood-based estimation. Another common limitation shared by many of the existing methods is the insufficient account for the interconnections among covariates. In this study, we adopt a least absolute deviation loss function to achieve robustness. For the selection of relevant covariates, a penalization approach is adopted. Significantly advancing from the existing literature, we describe the interconnections among covariates using a network structure and develop novel penalties to accommodate the network connectivity and connection measures. Consistency properties are rigorously established. Numerical studies, including both simulations and data analysis, demonstrate the competitive practical performance of the proposed method.