商品描述
The contributions in this book offer new insights into the theoretical and practical challenges of supervised and unsupervised learning, highlighting the remarkable breadth of contemporary statistical research while maintaining methodological rigor. Innovative approaches to statistical modeling, addressing spatial dependencies and circular data structures, are presented alongside significant advances in interpretable machine learning that reconcile statistical precision with algorithmic transparency. Particularly noteworthy is the volume's treatment of complex data structures, including novel methods for network analysis, high-dimensional clustering, temporal pattern recognition and optimization techniques. The volume interweaves methodological innovation and practical relevance, and the applications span diverse domains, including the social sciences and biomedical engineering, each demonstrating the effective translation of statistical theory into real-world impact. The book contains peer-reviewed contributions presented at the special edition of the 15th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, namely the International Scientific Joint Meeting of the Italian and Dutch-Flemish Classification Societies (CLADAG-VOC 2025), held in Naples, Italy, September 8-10, 2025. The conference provided fresh perspectives on the current state of research in clustering, classification and data analysis, and underpinned the value and significance of international collaboration, addressing the emerging needs of an increasingly complex data landscape and offering novel solutions to long-standing challenges in statistical data analysis.
商品描述(中文翻譯)
本書中的貢獻提供了對監督學習和非監督學習的理論與實踐挑戰的新見解，突顯了當代統計研究的廣泛性，同時保持了方法論的嚴謹性。書中介紹了針對空間依賴性和圓形數據結構的統計建模創新方法，以及在可解釋機器學習方面的重大進展，這些進展調和了統計精確性與算法透明性。特別值得注意的是，本書對複雜數據結構的處理，包括網絡分析、新穎的高維聚類、時間模式識別和優化技術的方法。該書將方法論創新與實踐相關性交織在一起，應用範圍涵蓋社會科學和生物醫學工程等多個領域，每個領域都展示了統計理論有效轉化為現實影響的能力。
本書包含了在意大利統計學會分類與數據分析小組第15屆科學會議特刊中發表的經過同行評審的貢獻，即意大利與荷蘭-弗拉芒分類學會的國際科學聯合會議（CLADAG-VOC 2025），該會議於2025年9月8日至10日在意大利那不勒斯舉行。會議提供了對聚類、分類和數據分析研究現狀的新視角，並強調了國際合作的價值和重要性，應對日益複雜的數據環境的需求，並為長期存在的統計數據分析挑戰提供了新穎的解決方案。
作者簡介
Antonio D'Ambrosio is a Full Professor in Statistics at the Department of Economics and Statistics of the University of Naples Federico II, Italy. His main research interests are in classification, clustering, non-parametric and computational statistics, regression modeling, and preference learning theory and modeling. Mark de Rooij is a Full Professor of Artificial Intelligence & Data Theory at the Institute of Psychology of Leiden University, the Netherlands. His research interests are in three main areas: predictive psychometrics, regression models for categorical response variables, and longitudinal data analysis. Kim De Roover is an Associate Professor in the Research group of Quantitative Psychology and Individual Differences, KU Leuven, Belgium. Her research interests are in factor analysis, structural equation modeling, measurement invariance, multigroup modeling, and mixture modeling. Carmela Iorio is an Associate Professor in Statistics at the Department of Economics and Statistics, University of Naples Federico II, Italy. Her main research interests are in the development of non-parametric statistical tools for financial time series, clustering, classification, and preference rankings theory and modeling. Michele La Rocca is a Full Professor of Statistics at the Departments of Economics and Statistics, University of Salerno, Italy. His research interests are in resampling techniques, empirical likelihood, neural networks, deep learning and extreme learning machines, robust and nonparametric inference, nonlinear time series analysis, and variable selection.
作者簡介(中文翻譯)
Antonio D'Ambrosio 是義大利那不勒斯費德里科二世大學經濟與統計系的全職教授。他的主要研究興趣包括分類、聚類、非參數與計算統計、迴歸建模，以及偏好學習理論與建模。 Mark de Rooij 是荷蘭萊頓大學心理學研究所的人工智慧與數據理論全職教授。他的研究興趣主要集中在三個領域：預測心理測量學、針對類別反應變數的迴歸模型，以及縱向數據分析。 Kim De Roover 是比利時魯汀大學定量心理學與個體差異研究小組的副教授。她的研究興趣包括因素分析、結構方程模型、測量不變性、多群體建模，以及混合建模。 Carmela Iorio 是義大利那不勒斯費德里科二世大學經濟與統計系的副教授。她的主要研究興趣在於為金融時間序列開發非參數統計工具、聚類、分類，以及偏好排名理論與建模。 Michele La Rocca 是義大利薩萊諾大學經濟與統計系的全職教授。他的研究興趣包括重抽樣技術、經驗似然、神經網絡、深度學習與極端學習機、穩健與非參數推斷、非線性時間序列分析，以及變數選擇。
