Sparse sliced inverse regression for high dimensional data analysis

Sparse sliced inverse regression for high dimensional data analysis

Background: Dimension reduction and variable selection play a critical role in the analysis of contemporary high-dimensional data. The semi-parametric multi-index model often serves as a reasonable model for analysis of such high-dimensional data. The sliced inverse regression (SIR) method, which ca...

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Bibliographic Details
Main Authors: Hilafu, H. (Author), Safo, S.E (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
article
Clustering algorithms
data analysis
decomposition
Dimension reduction
dimensionality reduction
discriminant analysis
Discriminant analysis
Eigenvalues and eigenfunctions
Generalized eigenvalue decomposition
Generalized eigenvalues
High dimensional data
High-dimensional data
Inverse problems
Linear discriminant analysis
Linear discriminant analyze
metabolomics
Multi-index
prediction
Regression analysis
Semiparametric
Semiparametric model
Semi-parametric modeling
simulation
Sliced inverse regression
Sliced inverse regressions
Variables selections
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