Modeling subpopulations for hierarchically structured data

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2023-11-22 DOI:10.1002/sam.11650

Andrew Simpson, Semhar Michael, Dylan Borchert, Christopher Saunders, Larry Tang

引用次数: 0

Abstract

The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates an additional layer of complexity. Hence, the data has a hierarchical structure in addition to the existence of underlying subpopulations. Finite mixtures are known for modeling heterogeneity; however, previous parameter estimation procedures assume that the data is generated through a simple random sampling process. We propose using a semi-supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same source, yet an unknown subpopulation. A simulation study and a real data analysis based on famous glass datasets and a keystroke dynamic typing data set show that the proposed approach performs better than other approaches that have been used previously in practice.

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为分层结构数据建模子种群

法医统计领域提供了一种独特的分层数据结构，其中总体由几个来源的子总体组成，并从每个来源收集样本。这种亚种群结构增加了一层复杂性。因此，除了存在潜在的子种群之外，数据还具有层次结构。有限混合以模拟异质性而闻名;然而，之前的参数估计过程假设数据是通过简单的随机抽样过程生成的。我们建议使用半监督混合建模方法来模拟亚种群结构，该方法利用我们知道样本收集来自同一来源，但未知的亚种群这一事实。基于著名玻璃数据集和按键动态打字数据集的仿真研究和实际数据分析表明，该方法比以往使用的其他方法具有更好的性能。

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

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.