Ranking 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.

In this thoroughly revised and expanded second edition, Statistical Methods and Machine Learning for Ranking Data 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. 

New 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. 

Combining 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.

Features of the Second Edition

• Comprehensive coverage of classical statistical methods for ranking data.
• Unified treatment of rank correlation, agreement testing, and hypothesis testing.
• Methods for complete, incomplete, and tied rankings.
• Expanded coverage of probabilistic and distance-based ranking models.
• New chapters on angle-based models, nonparametric Bayes, boosting, social network models, and deep preference learning.
• Applications drawn from surveys, recommendation systems, social networks, and preference analysis.
• Practical examples, datasets, and computational tools throughout

Dr. 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.

Since 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.

The 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.

This rich combination of academic scholarship and real-world experience informs his contributions to the field and his approach to teaching and research.

Dr. 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.

An 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.

Dr. 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.