logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

High-dimensional subgroup functional quantile regression with panel and dependent data

delete2025-09-01
delete0
PRE
AI
Y
Yu, Xiao-Ge
L
Liang, Han-Ying *
DOI：10.1016/j.csda.2025.108268delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
High-dimensional additive functional partial linear single-index quantile regression with high-dimensional parameters under subgroup panel data is investigated. Based on spline-based approach, we construct oracle estimators of the unknown parameter and functions, and discuss their consistency with rates and asymptotic normality under alpha-mixing assumptions. A penalized estimation method by using the SCAD technique is introduced to estimate the additive functions and parameter, enabling variable selection and automatic identification of the number of groups. Hypothesis testing for the parameter is also considered, and the asymptotic distributions of the restricted estimators and the test statistic are derived under both the null and local alternative hypotheses. Simulation studies and real data analysis are conducted to verify the validity of the proposed methods and applications.
Keywords:
Functional quantile regression
High-dimensional
Panel data
Single-index
alpha-mixing

Journal

C
Computational Statistics and Data Analysis
IF:
1.6
Papers:
12
Citations:
1.0W

Organization

No organization information available