Individualized Multidirectional Variable Selection
Individualized Multidirectional Variable Selection
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DOI：
10.1080/01621459.2019.1705308
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发表时间：
2017-09
影响因子：
3.7
通讯作者：
Xiwei Tang;F. Xue;A. Qu
中科院分区：
文献类型：
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作者：
Xiwei Tang;F. Xue;A. Qu
摘要本文提出了一种异构建模框架，该框架同时实现了个体特征选择和异构协变量效应子分组。与传统的模型选择方法相比，新方法构造了一种具有多方向收缩的分离惩罚，便于个体化建模，区分强信号和噪声信号，并为不同个体选择不同的相关变量。同时，该模型能够识别出具有相似协变量效应的个体，从而提高了个体化估计效率和特征选择精度。此外，该模型还结合了纵向数据的个体内相关性，以获得额外的效率。我们提供了一个一般的理论基础下的双发散建模框架，其中的个人数量和个人明智的测量的数量都可以发散，这使得在个人水平和人口水平上的推断。特别地，我们建立了强预言性质的个性化估计，以确保其最佳的大样本性质在各种条件下。一个有效的ADMM算法的计算可扩展性。模拟研究和应用创伤后精神障碍分析与遗传变异和艾滋病毒纵向治疗研究的说明，比较新的方法，现有的方法。本文的补充材料可在网上查阅。
Abstract In this article, we propose a heterogeneous modeling framework which achieves individual-wise feature selection and heterogeneous covariates’ effects subgrouping simultaneously. In contrast to conventional model selection approaches, the new approach constructs a separation penalty with multidirectional shrinkages, which facilitates individualized modeling to distinguish strong signals from noisy ones and selects different relevant variables for different individuals. Meanwhile, the proposed model identifies subgroups among which individuals share similar covariates’ effects, and thus improves individualized estimation efficiency and feature selection accuracy. Moreover, the proposed model also incorporates within-individual correlation for longitudinal data to gain extra efficiency. We provide a general theoretical foundation under a double-divergence modeling framework where the number of individuals and the number of individual-wise measurements can both diverge, which enables inference on both an individual level and a population level. In particular, we establish a strong oracle property for the individualized estimator to ensure its optimal large sample property under various conditions. An efficient ADMM algorithm is developed for computational scalability. Simulation studies and applications to post-trauma mental disorder analysis with genetic variation and an HIV longitudinal treatment study are illustrated to compare the new approach to existing methods. Supplementary materials for this article are available online.