Variable and boundary selection for functional data via multiclass logistic regression modeling

DOI10.1016/j.csda.2014.04.015zbMath1506.62127OpenAlexW1981426103MaRDI QIDQ1623638

Hidetoshi Matsui

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2324/26121

zbMATH Keywords

regularizationmodel selectionfunctional data analysisLassologistic regression model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Generalized linear models (logistic models) (62J12)

Related Items (12)

Feature selection for functional data ⋮ An adaptive group Lasso approach for domain selection in functional generalized linear models ⋮ General linear hypothesis testing in functional response model ⋮ Grouped variable importance with random forests and application to multiple functional data analysis ⋮ Sparse group lasso for multiclass functional logistic regression models ⋮ Robust multivariate functional discriminant coordinates ⋮ Selection of Effects in Cox Frailty Models by Regularization Methods ⋮ Fréchet distance-based cluster analysis for multi-dimensional functional data ⋮ Sparse clustering of functional data ⋮ Functional variable selection via Gram–Schmidt orthogonalization for multiple functional linear regression ⋮ Variable selection in functional regression models: a review ⋮ A note on variable selection in functional regression via random subspace method

Uses Software

• fda (R)
• glmnet

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