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Targeted Learning Causal Inference by Laan Mark (37 results)
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Targeted Learning in Data Science. Causal Inference for Complex Longitudinal Studies.
Book 149 of 160: Springer Series in StatisticsSeller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
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XLII, 640 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Springer Series in Statistics. Sprache: Englisch.
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Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies
Book 149 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)
Book 124 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)
Book 149 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Targeted Learning : Causal Inference for Observational and Experimental Data
Book 124 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies
Book 149 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Taschenbuch. Condition: Neu. Targeted Learning in Data Science | Causal Inference for Complex Longitudinal Studies | Mark J. Van Der Laan (u. a.) | Taschenbuch | xlii | Englisch | 2018 | Springer | EAN 9783030097363 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)
Book 124 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Targeted Learning : Causal Inference for Observational and Experimental Data
Book 124 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook for graduate students in statistics, data science, and public health dealswith the practical challenges that come with big, complex, and dynamic data. It presentsa scientific roadmap to translate real-world data science applications into formal statisticalestimation problems by using the general template of targeted maximum likelihoodestimators. These targeted machine learning algorithms estimate quantities of interestwhile still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniquescan answer complex questions including optimal rules for assigning treatment basedon longitudinal data with time-dependent confounding, as well as other estimands independent data structures, such as networks. Included in Targeted Learning in DataScience are demonstrations with soft ware packages and real data sets that present acase that targeted learning is crucial for the next generation of statisticians and datascientists. Th is book is a sequel to the first textbook on machine learning for causalinference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics andStatistics at UC Berkeley. His research interests include statistical methods in genomics,survival analysis, censored data, machine learning, semiparametric models, causalinference, and targeted learning. Dr. van der Laan received the 2004 Mortimer SpiegelmanAward, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005COPSS Presidential Award, and has graduated over 40 PhD students in biostatisticsand statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at HarvardMedical School. Her work is centered on developing and integratinginnovative statisticalapproaches to advance human health. Dr. Rose's methodological research focuseson nonparametric machine learning for causal inference and prediction. She co-leadsthe Health Policy Data Science Lab and currently serves as an associate editor for theJournal of the American Statistical Association and Biostatistics.
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Hardcover. Condition: Brand New. 1st edition. 640 pages. 9.25x6.25x1.75 inches. In Stock.
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Taschenbuch. Condition: Neu. Targeted Learning | Causal Inference for Observational and Experimental Data | Mark J. Van Der Laan (u. a.) | Taschenbuch | Springer Series in Statistics | lxxii | Englisch | 2013 | Humana | EAN 9781461429111 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Targeted Learning : Causal Inference for Observational and Experimental Data
Book 124 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 217.99
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Targeted Learning : Causal Inference for Observational and Experimental Data
Book 124 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 218.99
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
