Empirical Processes and Statistical Reinforcement Learning

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.