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

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  • Yu, Xiao-Ge
  • Liang, Han-Ying

Abstract

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 α-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.

Suggested Citation

  • Yu, Xiao-Ge & Liang, Han-Ying, 2026. "High-dimensional subgroup functional quantile regression with panel and dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:csdana:v:214:y:2026:i:c:s0167947325001446
    DOI: 10.1016/j.csda.2025.108268
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    References listed on IDEAS

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