商品詳細

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