The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models

Nava, Consuelo Rubina; Riso, Luigi; Zoia, Maria Grazia

doi:10.48550/arxiv.2211.07267

2022

DOI: 10.48550/arxiv.2211.07267

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The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models

Consuelo Rubina Nava¹,

Luigi Riso²,

Maria Grazia Zoia³

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2024

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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

View full text Add to dashboard Cite

show abstract

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The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models

Cited by 1 publication

References 0 publications

Conditional sufficient variable selection with prior information

Conditional sufficient variable selection with prior information

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