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dc.contributor.authorMengque Liu
dc.contributor.authorQingzhao Zhang
dc.contributor.authorKuangnan Fang
dc.contributor.authorShuangge Ma
dc.date.accessioned2020-10-10T02:41:02Z
dc.date.available2020-10-10T02:41:02Z
dc.date.issued2019-12-05
dc.identifier.citationComputational Statistics and Data Analysis,2019,
dc.identifier.other10.1016/j.csda.2019.106883
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/175254
dc.description.abstractAbstract(#br)The finite mixture of regression (FMR) model is a popular tool for accommodating data heterogeneity. In the analysis of FMR models with high-dimensional covariates, it is necessary to conduct regularized estimation and identify important covariates rather than noises. In the literature, there has been a lack of attention paid to the differences among important covariates, which can lead to the underlying structure of covariate effects. Specifically, important covariates can be classified into two types: those that behave the same in different subpopulations and those that behave differently. It is of interest to conduct structured analysis to identify such structures, which will enable researchers to better understand covariates and their associations with outcomes. Specifically, the FMR model with high-dimensional covariates is considered. A structured penalization approach is developed for regularized estimation, selection of important variables, and, equally importantly, identification of the underlying covariate effect structure. The proposed approach can be effectively realized, and its statistical properties are rigorously established. Simulation demonstrates its superiority over alternatives. In the analysis of cancer gene expression data, interesting models/structures missed by the existing analysis are identified.
dc.language.isozh_CN
dc.subjectFinite mixture of regression model
dc.subjectStructure of covariate effect
dc.subjectHigh-dimensional data
dc.titleStructured analysis of the high-dimensional FMR model
dc.typeArticle


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