Communication-efficient estimation and inference for high-dimensional longitudinal data

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational Statistics & Data Analysis Pub Date : 2025-02-24 DOI:10.1016/j.csda.2025.108154

Xing Li, Yanjing Peng, Lei Wang

引用次数: 0

Abstract

With the rapid growth in modern science and technology, distributed longitudinal data have drawn attention in a wide range of aspects. Realizing that not all effects of covariates are our parameters of interest, we focus on the distributed estimation and statistical inference of a pre-conceived low-dimensional parameter in the high-dimensional longitudinal GLMs with canonical links. To mitigate the impact of high-dimensional nuisance parameters and incorporate the within-subject correlation simultaneously, a decorrelated quadratic inference function is proposed for enhancing the estimation efficiency. Two communication-efficient surrogate decorrelated score estimators based on multi-round iterative algorithms are proposed. The error bounds and limiting distribution of the proposed estimators are established and extensive numerical experiments demonstrate the effectiveness of our method. An application to the National Longitudinal Survey of Youth Dataset is also presented.

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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 [...]

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