文献详情
Groupwise sufficient dimension reduction via conditional distance clustering
文献类型:期刊
通讯作者:Zhang, JX (reprint author), Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing, Beijing, Peoples R China.
期刊名称:METRIKA影响因子和分区
年:2020
卷:83
期:2
页码:217-242
ISSN:0026-1335
关键词:Sufficient dimension reduction; Group structure; Conditional independence; Conditional distance clustering
所属部门:统计学院
摘要:It becomes increasingly common to incorporate the predictors' grouping knowledge into dimension reduction techniques. In this article, we establish a complete framework named groupwise sufficient dimension reduction via conditional distance clustering, when the grouping information is unknown. We introduce a simple-type conditional dependence measurement and a corresponding conditional independence test. A clustering procedure based on the measurement and test is constructed to detect the suitab ...More
It becomes increasingly common to incorporate the predictors' grouping knowledge into dimension reduction techniques. In this article, we establish a complete framework named groupwise sufficient dimension reduction via conditional distance clustering, when the grouping information is unknown. We introduce a simple-type conditional dependence measurement and a corresponding conditional independence test. A clustering procedure based on the measurement and test is constructed to detect the suitable group structure. Finally we conduct sufficient dimension reduction under the obtained structure. Both simulations and a real data analysis demonstrate that the clustering strategy is effective, and the groupwise sufficient dimension reduction method is generally superior to the classical sufficient dimension reduction method. ...Hide
DOI:10.1007/s00184-019-00732-7
百度学术:Groupwise sufficient dimension reduction via conditional distance clustering
语言:外文
人气指数:2
浏览次数:2
作者其他论文
Regularization methods for high-dimensional sparse control function models.Xu, Xinyi, Li, Xiangjie, Zhang, Jingxiao,.JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2020, 206, 111-126.
Regularization methods for high-dimensional sparse control function models.Xu, Xinyi, Li, Xiangjie, Zhang, Jingxiao,.JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2020, 206, 111-126.
Ultrahigh dimensional feature screening for additive model with multivariate response.Liu, Shishi, Li, Xiangjie, Zhang, Jingxiao,.JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. 2020, 90(5), 775-799.
Feature screening for ultrahigh-dimensional additive logistic models.Wang, Lei, Ma, Xuejun, Zhang, Jingxiao,.JOURNAL OF STATISTICAL PLANNING AND INFERENCE. 2020, 205, 306-317.
UTILITY INDIFFERENCE PRICING OF PRODUCTS INTEGRATING REVERSE MORTGAGE WITH LONG-TERM CARE INSURANCE UNDER A LEVY PROCESS FINANCIAL MARKET.Ma, Lina;Zhang, Jingxiao;Kannan, D..DYNAMIC SYSTEMS AND APPLICATIONS.2013,22(2-3,SI),459-477.
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Groupwise sufficient dimension reduction via conditional distance clustering
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