重尾矩阵变量隐马尔可夫模型

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2025-05-12 DOI:10.1016/j.csda.2025.108198

Salvatore D. Tomarchio

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引用次数: 0

摘要

将隐马尔可夫模型（hmm）的矩阵变量框架扩展为使用矩阵变量t和污染正态分布的两类模型。这些模型改进了尾部行为、聚类的处理，并解决了在矩阵变量数据中识别离群矩阵的挑战。在R包MatrixHMM中实现了两种期望-条件最大化（ECM）算法用于参数估计。模拟评估参数恢复，鲁棒性，异常检测，并显示优于替代方法的优势。这些模型被应用于现实世界的数据，以分析意大利各省的劳动力市场动态。

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Heavy-tailed matrix-variate hidden Markov models

The matrix-variate framework for hidden Markov models (HMMs) is expanded with two families of models using matrix-variate t and contaminated normal distributions. These models improve the handling of tail behavior, clustering, and address challenges in identifying outlying matrices in matrix-variate data. Two Expectation-Conditional Maximization (ECM) algorithms are implemented in the R package MatrixHMM for parameter estimation. Simulations assess parameter recovery, robustness, anomaly detection, and show the advantages over alternative approaches. The models are applied to real-world data to analyze labor market dynamics across Italian provinces.

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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 [...]