| 書目名稱 | Statistical Methods for Dynamic Treatment Regimes |
| 副標(biāo)題 | Reinforcement Learni |
| 編輯 | Bibhas Chakraborty,Erica E.M. Moodie |
| 視頻video | http://file.papertrans.cn/877/876516/876516.mp4 |
| 概述 | Pioneering review of DTRs to date through theory, explanation of concepts, and applications.Covers newest statistical and computational approaches to the development of dynamic treatment regime models |
| 叢書名稱 | Statistics for Biology and Health |
| 圖書封面 |  |
| 描述 | .Statistical Methods for Dynamic Treatment Regimes. shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors prov |
| 出版日期 | Textbook 2013 |
| 關(guān)鍵詞 | Causal inference; Dynamic treatments; Personalized medicine; Reinforcement learning; Statistical methods |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4614-7428-9 |
| isbn_softcover | 978-1-4899-9030-3 |
| isbn_ebook | 978-1-4614-7428-9Series ISSN 1431-8776 Series E-ISSN 2197-5671 |
| issn_series | 1431-8776 |
| copyright | Springer Science+Business Media New York 2013 |
|Archiver|手機(jī)版|小黑屋|
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