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標(biāo)題: Titlebook: Statistical Methods for Dynamic Treatment Regimes; Reinforcement Learni Bibhas Chakraborty,Erica E.M. Moodie Textbook 2013 Springer Science [打印本頁]

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書目名稱Statistical Methods for Dynamic Treatment Regimes讀者反饋學(xué)科排名





作者: 訓(xùn)誡    時間: 2025-3-21 22:18
Textbook 2013amiliarity 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
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978-1-4899-9030-3Springer Science+Business Media New York 2013
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Statistical Methods for Dynamic Treatment Regimes978-1-4614-7428-9Series ISSN 1431-8776 Series E-ISSN 2197-5671
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作者: 芳香一點(diǎn)    時間: 2025-3-22 15:37
Estimation of DTRs for Alternative Outcome Types,In this chapter, we consider the estimation of dynamic treatment regimes for a variety of outcome types, including multi-dimensional continuous outcomes, time-to-event outcomes in the presence of censoring, and discrete outcomes. Methods discussed include Q-learning, marginal structural models, and a fully parametric, likelihood-based approach.
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Additional Considerations and Final Thoughts,The statistical study of DTRs and associated methods of estimation is a young and growing field. As such, there are many topics which are only beginning to be explored. In this chapter, we point to some new developments and areas of research in the field.
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1431-8776 oaches to the development of dynamic treatment regime models.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 demo
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Statistical Reinforcement Learning,sequence of treatments. This problem bears strong resemblance to the problem of reinforcement learning in computer science, a branch of machine learning that deals with the problem of multi-stage, sequential decision making by a learning agent. In this chapter, we review the necessary concepts of re
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Estimation of Optimal DTRs by Directly Modeling Regimes,ling the conditional mean outcome: inverse probability of treatment weighting, marginal structural models, and classification-based methods. The fundamental difference between the approaches considered in the current chapter and those considered in previous chapters (e.g. Q-learning and G-estimation
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Inference and Non-regularity,he optimal treatments at subsequent stages are non-unique for at least some strictly positive proportion of subjects in the population. We discuss and illustrate the phenomenon using Q-learning and G-estimation, and propose a number of strategies to mitigate the non-regularity including thresholding
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Statistical Reinforcement Learning,inforcement learning, connect them to the relevant statistical literature, and develop a mathematical framework that will enable us to treat the problem of estimating the optimal dynamic treatment regimes rigorously.
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Textbook 2013ference 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
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The Data: Observational Studies and Sequentially Randomized Trials,rform valid analyses in each, along with some examples. We also discuss a basic framework of causal inference in the context of observational studies, and power and sample size issues in the context of randomized studies.
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Estimation of Optimal DTRs by Directly Modeling Regimes,mental difference between the approaches considered in the current chapter and those considered in previous chapters (e.g. Q-learning and G-estimation) lies in the primary target of estimation (and inference): the methods considered presently target the parameters of the decision rule itself.
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Inference and Non-regularity, illustrate the phenomenon using Q-learning and G-estimation, and propose a number of strategies to mitigate the non-regularity including thresholding and penalization of the estimators as well as non-standard implementations of the bootstrap.
作者: CHARM    時間: 2025-3-25 23:05
Lennart Rohlfs,Julien Weiss the company or even reshaping the forces and direction of a whole industry. What allows strategic leaders make great decisions? Is this pure chance or a skill that has been developed through many years of experience? We argue that it is strategic leadership wisdom manifesting in the leaders’ abilit
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Melina Bosco,Marco Caragliano,Edoardo M. Marino,Pier Paolo Rossin we present characterization theorems for three types of problems: Finite dimensional, variational and problems in linear topological spaces. In each case we present theorems for equality — inequality constraints. The theorems in each case are: first order necessary conditions, first order sufficie
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Multi-Object Tracking based on Imaging Radar 3D Object Detection,vior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learningbased object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately o
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Textbook 2001mperature regulation and current concepts of the central nervous interface between temperature signals generated in the body and control mechanisms are examined in detail. This book is of invaluable help for undergraduates, postgraduates, teachers, physicians and scientists.
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Michael Bronzite BSc, MSc, MBCS, CEngered for performance evaluation using Ling-Spam and SpamAssassin databases. Experimental results show that the PSO-based feature selection algorithm was presented to generate excellent feature selection results with the minimal set of selected features to be caused by a high accuracy of spam email c
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A Compartmental Mathematical Model of COVID-19 Intervention Scenarios for Mumbai,nario. The proposed model predicts the approximate time when the disease is at its worst and the approximate time when death cases barely arise and determines how many hospital beds in the peak days of infection would be expected. The proposed model outperforms the classic ARX, SARIMAX, and the ARIM
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Front Matterotrophic communities in acidic environments have been examined using cultivation-based physiological analyses as well as cultivation-independent molecular approaches, including omic-technologies. Most investigations have focused on moderately acidophilic, aerobic methanotrophs belonging to the phylu
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