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

IF 1.6 3区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiao-Ge Yu, Han-Ying Liang
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引用次数: 0

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.
高维亚群功能分位数回归与面板和相关数据
研究了子群面板数据下具有高维参数的高维加性泛函偏线性单指标分位数回归。基于样条方法构造了未知参数和函数的oracle估计量,并讨论了它们在α-混合假设下与速率的一致性和渐近正态性。引入了一种基于SCAD技术的惩罚估计方法来估计加性函数和参数,实现了组数的变量选择和自动识别。考虑了参数的假设检验,导出了零假设和局部备用假设下的限制估计量和检验统计量的渐近分布。通过仿真研究和实际数据分析,验证了所提方法及其应用的有效性。
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来源期刊
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis 数学-计算机:跨学科应用
CiteScore
3.70
自引率
5.60%
发文量
167
审稿时长
60 days
期刊介绍: Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]
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