A dual-penalized approach to hypothesis testing in high-dimensional linear mediation models

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2024-09-24 DOI:10.1016/j.csda.2024.108064

引用次数: 0

Abstract

The field of mediation analysis, specifically high-dimensional mediation analysis, has been arousing great interest due to its applications in genetics, economics and other areas. Mediation analysis aims to investigate how exposure variables influence outcome variable via mediators, and it is categorized into direct and indirect effects based on whether the influence is mediated. A novel hypothesis testing method, called the dual-penalized method, is proposed to test direct and indirect effects. This method offers mild conditions and sound theoretical properties. Additionally, the asymptotic distributions of the proposed estimators are established to perform hypothesis testing. Results from simulation studies demonstrate that the dual-penalized method is highly effective, especially in weak signal settings. Further more, the application of this method to the childhood trauma data set reveals a new mediator with a credible basis in biological processes.

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高维线性中介模型假设检验的双重惩罚方法

中介分析，特别是高维中介分析，因其在遗传学、经济学等领域的应用而备受关注。中介分析旨在研究暴露变量如何通过中介影响结果变量，根据影响是否被中介分为直接影响和间接影响。本文提出了一种新的假设检验方法，即双重惩罚法，用于检验直接效应和间接效应。该方法条件温和，理论性强。此外，还建立了所提估计值的渐近分布，以进行假设检验。模拟研究结果表明，双惩罚法非常有效，尤其是在弱信号环境下。此外，该方法在儿童创伤数据集中的应用揭示了一个新的中介因子，它在生物过程中具有可信的基础。

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 [...]