{"product_id":"9781071656549","title":"Statistical Methods and Machine Learning for Ranking Data","description":"\u003ch3\u003eFrontiers in Probability and the Statistical Sciences\u003c\/h3\u003e\u003ch1\u003eStatistical Methods and Machine Learning for Ranking Data\u003c\/h1\u003e\u003ch3\u003eMayer Alvo | Philip L. H. Yu\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eMathematics \/ Probability \u0026amp; Statistics \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eRanking data arise whenever individuals, organizations, or intelligent systems express preferences by ordering alternatives—from consumer choices and election results to recommendation systems and machine learning applications. Understanding and analyzing such data requires specialized statistical methods that go beyond traditional approaches.\u003c\/p\u003e\r\n\u003cp\u003eIn this thoroughly revised and expanded \u003cstrong\u003esecond edition\u003c\/strong\u003e, \u003cem\u003eStatistical Methods and Machine Learning for Ranking Data\u003c\/em\u003e provides a comprehensive treatment of the theory, methodology, and modern applications of ranking data analysis. The book develops the foundations of rank correlation through distance-based approaches, introduces the concept of compatibility for incomplete and tied rankings, and presents a unified framework for hypothesis testing involving ranking data. Readers are guided through methods for exploratory analysis, correlation assessment, agreement testing, experimental design, ordered alternatives, and probabilistic models for rankings. \u003c\/p\u003e\r\n\u003cp\u003eNew to this edition is a substantial expansion into contemporary machine learning. The book now includes dedicated coverage of decision tree methods for ranking data, weighted and mixture distance-based models, boosting algorithms, social network-based preference models, Bayesian approaches for ranking and recommendation systems, and cutting-edge deep preference learning using graph neural networks. These additions reflect the growing importance of ranking methodologies in data science, artificial intelligence, recommender systems, and network analysis. \u003c\/p\u003e\r\n\u003cp\u003eCombining rigorous statistical foundations with modern computational techniques, the book illustrates key ideas through real-world datasets, practical examples, and software resources. It serves as both a graduate-level text and a valuable reference for researchers and practitioners in statistics, data science, machine learning, marketing research, social sciences, and related disciplines.\u003c\/p\u003e\r\n\u003cp\u003e\u003cstrong\u003eFeatures of the Second Edition\u003c\/strong\u003e\u003c\/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eComprehensive coverage of classical statistical methods for ranking data.\u003c\/li\u003e\r\n\u003cli\u003eUnified treatment of rank correlation, agreement testing, and hypothesis testing.\u003c\/li\u003e\r\n\u003cli\u003eMethods for complete, incomplete, and tied rankings.\u003c\/li\u003e\r\n\u003cli\u003eExpanded coverage of probabilistic and distance-based ranking models.\u003c\/li\u003e\r\n\u003cli\u003eNew chapters on angle-based models, nonparametric Bayes, boosting, social network models, and deep preference learning.\u003c\/li\u003e\r\n\u003cli\u003eApplications drawn from surveys, recommendation systems, social networks, and preference analysis.\u003c\/li\u003e\r\n\u003cli\u003ePractical examples, datasets, and computational tools throughout\u003c\/li\u003e\r\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eDr. Mayer Alvo is a mathematician and statistician whose career has spanned more than five decades of teaching, research, and consulting. He earned a B.Sc. in Mathematics (1967) and an M.Sc. in Statistics (1968) from McGill University, followed by a Ph.D. in Mathematical Statistics from Columbia University in 1972.\u003c\/p\u003e\r\n\u003cp\u003eSince 1973, Dr. Alvo has been a faculty member in the Department of Mathematics and Statistics at the University of Ottawa, where he has taught a wide range of undergraduate and graduate courses in both disciplines. In parallel with his academic career, he has served as a consultant on advanced research projects and scientific studies, bringing practical experience to complement his theoretical expertise.\u003c\/p\u003e\r\n\u003cp\u003eThe interplay between theory and application has shaped Dr. Alvo's scholarly work, resulting in more than 75 publications in peer-reviewed journals. His research interests span numerous areas of statistics, including nonparametric methods, sampling and survey techniques, experimental design, spatial statistics, environmental statistics, sequential analysis, and Bayesian methods.\u003c\/p\u003e\r\n\u003cp\u003eThis rich combination of academic scholarship and real-world experience informs his contributions to the field and his approach to teaching and research.\u003c\/p\u003e\r\n\u003cp\u003eDr. Philip L.H. Yu is Professor in the Department of Mathematics and Information Technology and Associate Director of the University Research Facility of Data Science and Artificial Intelligence at The Education University of Hong Kong. He also held the appointment of CRM–Simons Professor while visiting at the University of Ottawa, Canada.\u003c\/p\u003e\r\n\u003cp\u003eAn internationally recognized researcher, Dr. Yu currently serves on the Executive Committee of the International Association for Statistical Computing (IASC) and previously chaired its Asian Regional Section. His research spans a broad range of areas, including ranking data analysis, data science and artificial intelligence applications in education and healthcare, time series analysis, risk management, and environmental statistics.\u003c\/p\u003e\r\n\u003cp\u003eDr. Yu has published extensively in these fields and has contributed to the development of innovative statistical and machine learning methodologies. His research has also led to practical applications recognized through multiple international invention awards, reflecting his commitment to bridging theoretical advances with real-world impact.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e02 November 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer US\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781071656549\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer US","offers":[{"title":"Default Title","offer_id":51687875182732,"sku":"9781071656549","price":143.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781071656549","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}