{"title":"Statistical analysis of prediction in functional polynomial quantile regression","authors":"Hongzhi Tong","doi":"10.1016/j.jco.2025.101995","DOIUrl":null,"url":null,"abstract":"<div><div>We consider in this paper the quantile regression in a functional polynomial model, where the conditional quantile of a scalar response is modeled by a polynomial of functional predictor. It extends beyond the standard functional linear setting to accommodate more general functional polynomial model. A Tikhonov regularized functional polynomial quantile regression approach is introduced and investigated. By utilizing some techniques of empirical processes, we establish the explicit convergence rates of the prediction error of the proposed estimator under mild assumptions.</div></div>","PeriodicalId":50227,"journal":{"name":"Journal of Complexity","volume":"92 ","pages":"Article 101995"},"PeriodicalIF":1.8000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Complexity","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885064X25000731","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
We consider in this paper the quantile regression in a functional polynomial model, where the conditional quantile of a scalar response is modeled by a polynomial of functional predictor. It extends beyond the standard functional linear setting to accommodate more general functional polynomial model. A Tikhonov regularized functional polynomial quantile regression approach is introduced and investigated. By utilizing some techniques of empirical processes, we establish the explicit convergence rates of the prediction error of the proposed estimator under mild assumptions.
期刊介绍:
The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited.
Areas Include:
• Approximation theory
• Biomedical computing
• Compressed computing and sensing
• Computational finance
• Computational number theory
• Computational stochastics
• Control theory
• Cryptography
• Design of experiments
• Differential equations
• Discrete problems
• Distributed and parallel computation
• High and infinite-dimensional problems
• Information-based complexity
• Inverse and ill-posed problems
• Machine learning
• Markov chain Monte Carlo
• Monte Carlo and quasi-Monte Carlo
• Multivariate integration and approximation
• Noisy data
• Nonlinear and algebraic equations
• Numerical analysis
• Operator equations
• Optimization
• Quantum computing
• Scientific computation
• Tractability of multivariate problems
• Vision and image understanding.