Integrated subgroup identification from multi-source data

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational Statistics & Data Analysis Pub Date : 2024-01-11 DOI:10.1016/j.csda.2024.107918

Lihui Shao , Jiaqi Wu , Weiping Zhang , Yu Chen

引用次数: 0

Abstract

Subgroup identification is crucial in dealing with the heterogeneous population and has wide applications in various areas, such as clinical trials and market segmentation. With the prevalence of multi-source data, there is a practical need to identify subgroups based on multi-source data. This paper proposes a working-independence pseudo-loglikelihood and integrates the parameters of each source into a pairwise fusion penalty for simultaneous parameter estimation and subgroup identification. To implement the proposed method, an alternating direction method of multipliers (ADMM) algorithm is derived. Furthermore, the weak oracle properties of parameter estimation are established, illustrating the latent subgroups can be consistently identified. Finally, numerical simulations and an analysis of a randomized trial on reduced nicotine standards for cigarettes are conducted to evaluate the performance of the proposed method.

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从多源数据中综合识别分组

亚组识别对于处理异质人群至关重要，在临床试验和市场细分等多个领域有着广泛的应用。随着多源数据的普及，基于多源数据的亚组识别有了实际需求。本文提出了一种与工作无关的伪对数概率，并将每个来源的参数整合到一个成对融合惩罚中，以同时进行参数估计和亚组识别。为了实现所提出的方法，推导出了一种交替方向乘法（ADMM）算法。此外，还建立了参数估计的弱甲骨文特性，说明可以持续识别潜在子群。最后，对降低香烟尼古丁标准的随机试验进行了数值模拟和分析，以评估所提方法的性能。

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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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