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Variable Selection for Additive Quantile Regression with Nonlinear Interaction Structures

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Publicado en:Mathematics vol. 13, no. 9 (2025), p. 1522
Autor principal: Bai Yongxin
Otros Autores: Jiang Jiancheng, Tian Maozai
Publicado:
MDPI AG
Materias:
Data analysis
Regularization
Heredity
Smoothness
Regression models
Generalized linear models
Adultery
Monte Carlo simulation
Quantiles
Effectiveness
Fines & penalties
Feature selection
Regularization methods
Algorithms
Asymptotic properties
Complexity
Dimensional analysis
Parkinson's disease
Acceso en línea:Citation/Abstract
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Resumen:In high-dimensional data analysis, main effects and interaction effects often coexist, especially when complex nonlinear relationships are present. Effective variable selection is crucial for avoiding the curse of dimensionality and enhancing the predictive performance of a model. In this paper, we introduce a nonlinear interaction structure into the additive quantile regression model and propose an innovative penalization method. This method considers the complexity and smoothness of the additive model and incorporates heredity constraints on main effects and interaction effects through an improved regularization algorithm under marginality principle. We also establish the asymptotic properties of the penalized estimator and provide the corresponding excess risk. Our Monte Carlo simulations illustrate the proposed model and method, which are then applied to the analysis of Parkinson’s disease rating scores and further verify the effectiveness of a novel Parkinson’s disease (PD) treatment.
ISSN:2227-7390
DOI:10.3390/math13091522
Fuente:Engineering Database