 Integrated subgroup identification from multi-source dataLihui Shao, Jiaqi Wu, Weiping Zhang and Yu Chen Computational Statistics & Data Analysis, 2024, vol. 193, issue C 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. Keywords: Subgroup identification; Simultaneous analysis; Multi-source data; Concave penalization; Generalized linear model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:193:y:2024:i:c:s0167947324000021 DOI: 10.1016/j.csda.2024.107918 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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