Empirical Processes and Statistical Reinforcement Learning: A Festschrift in Honor of Michael R. Kosorok
暫譯: 經驗過程與統計強化學習：獻給 Michael R. Kosorok 的紀念文集

Ma, Shuangge, Laber, Eric B., Zhao, Yingqi

Empirical Processes and Statistical Reinforcement Learning: A Festschrift in Honor of Michael R. Kosorok

商品描述

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.

商品描述(中文翻譯)

Michael R. Kosorok 在生物統計學、精準醫療、機器學習與人工智慧領域皆有卓越貢獻，持續形塑統計方法學與生物醫學研究的未來。《Empirical Processes and Statistical Reinforcement Learning: A Festschrift in Honor of Michael R. Kosorok》以他的傑出成就為核心。

本書涵蓋經驗過程（empirical processes）、半參數推論（semiparametric inference）、因果推論（causal inference）、強化學習（reinforcement learning）、人工智慧（artificial intelligence）與精準醫療（precision medicine）等主題。本書由該領域的頂尖專家共同撰寫，突顯 Michael R. Kosorok 在推動癌症研究與治療策略之統計方法學發展上所扮演的關鍵角色。

本紀念文集既可作為研究人員的參考書，也適合作為生物統計學與生物醫學研究博士程度教育的學習資源。

主要特色：

• 闡述方法學發展的前沿，以及其在生物醫學領域的應用。
• 解說經驗過程與半參數推論，包括分散式系統中的 minimax 最優性與目標定位。
• 深入探討因果推論與強化學習，涵蓋公平表徵學習（fair representation learning）、合成控制模型（synthetic control models），以及具有未測量混淆因子的因果強化學習（causal reinforcement learning with unmeasured confounders）等主題。
• 展示精準醫療的最新進展，包括個人化治療規則（individualized treatment rules）、結果加權學習（outcome-weighted learning），以及在運動分析（sports analytics）中的應用。
• 收錄運用統計與機器學習方法進行臨床決策與早期偵測的相關研究。

作者簡介

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).

作者簡介(中文翻譯)

Shuangge Ma 是 Yale School of Public Health 的生物統計學教授。他曾是 University of Wisconsin Kosorok 教授的博士生，並與 Kosorok 教授共同研究半參數建模、生存分析與經驗過程。

Eric B. Laber 是 Duke University James B. Duke 傑出統計科學、生物統計學與生物資訊學教授。他是 American Statistical Association 和 International Statistical Institute 的會士，也是 Gottfried E. Noether Award、Raymond J. Carroll Award 及 American Statistical Association Outstanding Application Award 的得主。

Yingqi Zhao 於 2012 年取得 University of North Carolina at Chapel Hill 的生物統計學博士學位。目前，她是 Fred Hutchinson Cancer Center 的教授。她的研究著重於精準醫療、動態治療策略、臨床試驗設計、真實世界研究及機器學習的統計方法，應用領域包括癌症治療與預防，以及為複雜型第二型糖尿病患者提供醫療照護。她於 2012 年發表在 Journal of the American Statistical Association 的論文獲得廣泛引用，並為後續以生物標誌物引導治療與預防策略的研究奠定基礎。她運用電子病歷資料進行疾病監測決策的研究，獲得 2011 年 Biometrics Award 最佳論文獎。她的研究曾獲 National Institutes of Health (NIH)、Hope Foundation 及 Patient-Centered Outcomes Research Institute (PCORI) 多項獎助支持。

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