Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models

DOI10.1007/s42081-020-00090-zzbMath1477.62185OpenAlexW2998883130MaRDI QIDQ825321

Chien-Tong Lin, Toshio Honda

Publication date: 17 December 2021

Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)

Full work available at URL: http://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/30969/070econDP20-01.pdf

zbMATH Keywords

maximum likelihoodstopping rulevarying coefficient modelB-spline basisforward procedurescreening consistency

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Generalized linear models (logistic models) (62J12) Optimal stopping in statistics (62L15)

Related Items

Forward variable selection for ultra-high dimensional quantile regression models ⋮ Forward selection for feature screening and structure identification in varying coefficient models ⋮ Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sure independence screening in generalized linear models with NP-dimensionality
• A stepwise regression method and consistent model selection for high-dimensional sparse linear models
• Consistent model selection criteria for quadratically supported risks
• Statistics for high-dimensional data. Methods, theory and applications.
• Statistical methods with varying coefficient models
• Globally adaptive quantile regression with ultra-high dimensional data
• A selective overview of feature screening for ultrahigh-dimensional data
• Feature screening for generalized varying coefficient models with application to dichotomous responses
• Weak convergence and empirical processes. With applications to statistics
• Adaptively weighted group Lasso for semiparametric quantile regression models
• Extended BIC for small-n-large-P sparse GLM
• Forward Regression for Ultra-High Dimensional Variable Screening
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Model Selection via Bayesian Information Criterion for Quantile Regression Models
• Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Feature Screening in Ultrahigh Dimensional Generalized Varying-coefficient Models
• Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

This page was built for publication: Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models