Empirical Processes and Statistical Reinforcement Learning

A Festschrift in Honor of Michael R. Kosorok
1. Auflage 2026
ISBN: 978-1-032-85663-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 354 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 453 g

Michael R. Kosorok has made significant contributions to biostatistics, precision medicine, machine learning, and artificial intelligence, shaping the future of statistical methodology and biomedical research. Empirical Processes and Statistical Reinforcement Learning: A Festschrift in Honor of Michael R. Kosorok centers around his remarkable achievements.

This book encompasses topics such as empirical processes, semiparametric inference, causal inference, reinforcement learning, artificial intelligence, and precision medicine. With contributions from leading experts in the field, it highlights Michael R. Kosorok’s pivotal role in advancing statistical methodology for cancer research and treatment regimes.

This Festschrift serves both as a reference for researchers and as a resource for PhD-level education in biostatistics and biomedical research.

Key Features:

- Informs the frontiers of methodological developments and their biomedical applications.

- Explains empirical processes and semiparametric inference, including minimax optimality and target localization in distributed systems.

- Provides in-depth insights into causal inference and reinforcement learning with topics such as fair representation learning, synthetic control models, and causal reinforcement learning with unmeasured confounders.

- Showcases advancements in precision medicine, including individualized treatment rules, outcome-weighted learning, and applications in sports analytics.

- Includes contributions on statistical and machine learning methods for clinical decision-making and early detection.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Ma, Shuangge

Laber, Eric B.

Zhao, Yingqi

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Part I Empirical process and semiparametric inference. Chpater 1 A semi-parametric model for target localization in distributed systems. Chapter 2 Minimax Optimality of the Moderated MMD and Empirical Moderated MMD-Based Two-Sample Tests. Part II Causal inference, reinforcement learning, and artificial intelligence Chapter 3 Statistical Inference in Reinforcement Learning: A Selective Survey. Chapter 4 Fair Sufficient Representation Learning. Chapter 5 Efficiently Learning Synthetic Control Models for High-Dimensional Disaggregated Data. Chapter 6 A Selective Review on Causal Reinforcement Learning with Unmeasured Confounders. Chapter 7 Efficient learning using U-statistics with a valid instrumental variable. Part III Precision medicine. Chapter 8 Learning Individualized Treatment Rules with Optimal Treatment Grouping for Maximizing Mean Survival Time Chapter 9 Statistical and Machine Learning in Individualized Clinical Decision Rules: Applications in Early Detection. Chapter 10 Introduction to Outcome Weighted Learning for Optimal Treatment Regimes. Chapter 11 Precision Medicine Meets Sports Analytics: Promise, Pitfalls, and Lessons from the Field. Chapter 12 Optimal treatment strategies for prioritized outcomes.

Über Autor(innen)

Shuangge Ma is Professor of Biostatistics at the Yale School of Public Health. He was a Ph.D. student of Prof. Kosorok at the University of Wisconsin and worked with him on semiparametric modeling, survival analysis, and empirical processes.

Eric B. Laber is the James B. Duke Distinguished Professor of Statistical Sciences and Biostatistics and Bioinformatics at Duke University. He is a fellow of the American Statistical Association and the International Statistical Institute, as well as the recipient of the Gottfried E. Noether Award, the Raymond J. Carroll Award, and the American Statistical Association Outstanding Application Award.

Yingqi Zhao received her PhD in Biostatistics from the University of North Carolina at Chapel Hill in 2012. She is currently a Professor at Fred Hutchinson Cancer Center. Her research focuses on statistical methodologies for precision medicine, dynamic treatment regimes, clinical trial design, real-world studies, and machine learning, with applications in cancer therapeutics and prevention and health care delivery for patients with complex type 2 diabetes. Her 2012 publication in the Journal of the American Statistical Association has been widely cited and has provided a foundation for subsequent research on biomarker-guided treatment and prevention strategies. Her work on decision-making for disease surveillance using electronic medical record data was recognized with the 2011 Best Paper in Biometrics Award. Her research has been supported by multiple awards from the National Institutes of Health (NIH), the Hope Foundation, and the Patient-Centered Outcomes Research Institute (PCORI).