Heterogeneous quantile regression for longitudinal data with subgroup structures

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2024-01-29 DOI:10.1016/j.csda.2024.107928

Zhaohan Hou, Lei Wang

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引用次数: 0

Abstract

Subgroup analysis for modeling longitudinal data with heterogeneity across all individuals has drawn attention in the modern statistical learning. In this paper, we focus on heterogeneous quantile regression model and propose to achieve variable selection, heterogeneous subgrouping and parameter estimation simultaneously, by using the smoothed generalized estimating equations in conjunction with the multi-directional separation penalty. The proposed method allows individuals to be divided into multiple subgroups for different heterogeneous covariates such that estimation efficiency can be gained through incorporating individual correlation structure and sharing information within subgroups. A data-driven procedure based on a modified BIC is applied to estimate the number of subgroups. Theoretical properties of the oracle estimator given the underlying true subpopulation information are firstly provided and then it is shown that the proposed estimator is equivalent to the oracle estimator under some conditions. The finite-sample performance of the proposed estimators is studied through simulations and an application to an AIDS dataset is also presented.

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具有分组结构的纵向数据的异质性量子回归

对所有个体具有异质性的纵向数据进行分组分析建模已引起现代统计学习的关注。本文以异质性量化回归模型为研究对象，提出通过平滑广义估计方程结合多向分离惩罚，同时实现变量选择、异质性分组和参数估计。所提出的方法允许针对不同的异质性协变量将个体划分为多个子组，从而通过纳入个体相关结构和共享子组内信息来提高估计效率。该方法采用基于修正 BIC 的数据驱动程序来估算子群数量。首先给出了给定基本真实子群信息的神谕估计器的理论特性，然后证明了在某些条件下，所提出的估计器等同于神谕估计器。通过模拟研究了所提估计器的有限样本性能，并介绍了在艾滋病数据集上的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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 [...]