Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures

Dong, Yuexiao

doi:10.1007/978-3-030-69009-0_8

Festschrift in Honor of R. Dennis Cook 2021

DOI: 10.1007/978-3-030-69009-0_8

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Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures

Yuexiao Dong

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2024

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Cited by 2 publications

References 26 publications

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Conditional sufficient variable selection with prior information

Wang,

Lu,

Weng

et al. 2024

Comput Stat

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Dimension reduction and variable selection play crucial roles in high-dimensional data analysis. Numerous existing methods have been demonstrated to attain either or both of these goals. The Minimum Average Variance Estimation (MAVE) method and its variants are effective approaches to estimate directions on the Central Mean Subspace (CMS). The Sparse Minimum Average Variance Estimation (SMAVE) combines the concepts of sufficient dimension reduction and variable selection and has been demonstrated to exhaustively estimate CMS while simultaneously selecting informative variables using LASSO without assuming any specific model or distribution on the predictor variables. In many applications, however, researchers typically possess prior knowledge for a set of predictors that is associated with response. In the presence of a known set of variables, the conditional contribution of additional predictors provides a natural evaluation of the relative importance. Based on this concept, we propose the Conditional Sparse Minimum Average Variance Estimation (CSMAVE) method. By utilizing prior information and creating a meaningful conditioning set for SMAVE, we intend to select variables that will result in a more parsimonious model and a more accurate interpretation than SMAVE. We evaluate our strategy by analyzing simulation examples and comparing them to the SMAVE method. And a real-world dataset validates the applicability and efficiency of our method.

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Conditional sufficient variable selection with prior information

Wang,

Lu,

Weng

et al. 2024

Comput Stat

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show abstract

Deep nonlinear sufficient dimension reduction

Chen,

Jiao,

Qiu

et al. 2024

Ann. Statist.

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Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures

Cited by 2 publications

References 26 publications

Conditional sufficient variable selection with prior information

Conditional sufficient variable selection with prior information

Deep nonlinear sufficient dimension reduction

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