Orthogonality-projection-based penalized variable selection for high-dimensional partially linear models

Yiping Yang; Peixin Zhao; Jun Zhang

doi:10.1016/j.apm.2024.115785

ScienceGate Book Chapters

JOURNAL ARTICLE

Orthogonality-projection-based penalized variable selection for high-dimensional partially linear models

Yiping Yang Peixin Zhao Jun Zhang

Year: 2024 Journal: Applied Mathematical Modelling Vol: 138 Pages: 115785-115785 Publisher: Elsevier BV

DOI: 10.1016/j.apm.2024.115785

Get Full-Text PDF Get Analytical Report

Keywords:

Orthogonality Projection (relational algebra) Mathematics Feature selection Applied mathematics Variable (mathematics) Selection (genetic algorithm) Linear model Mathematical optimization Algorithm Computer science Mathematical analysis Statistics Artificial intelligence Geometry

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.21

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

Fault Detection and Control Systems

Physical Sciences → Engineering → Control and Systems Engineering

Orthogonality-projection-based penalized variable selection for high-dimensional partially linear models

Metrics

Topics

Related Documents

Orthogonality based penalized GMM estimation for variable selection in partially linear spatial autoregressive models

Double penalized variable selection for high-dimensional partial linear mixed effects models

SCAD-penalized regression in high-dimensional partially linear models

Double penalized variable selection procedure for partially linear models with longitudinal data

Penalized Empirical Likelihood based Variable Selection for Partially Linear Quantile Regression Models with Missing Responses