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Structured analysis of the high-dimensional FMR model

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  • Liu, Mengque
  • Zhang, Qingzhao
  • Fang, Kuangnan
  • Ma, Shuangge

Abstract

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.

Suggested Citation

  • Liu, Mengque & Zhang, Qingzhao & Fang, Kuangnan & Ma, Shuangge, 2020. "Structured analysis of the high-dimensional FMR model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302385
    DOI: 10.1016/j.csda.2019.106883
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    References listed on IDEAS

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    1. Yanguas Parra, Paola & Hauenstein, Christian & Oei, Pao-Yu, 2021. "The death valley of coal – Modelling COVID-19 recovery scenarios for steam coal markets," Applied Energy, Elsevier, vol. 288(C).

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