Isbn: 9783319653037 - targeted learning in data science: causal inference for complex longitudinal studies (springer series in statistics) (21 resultados)

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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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides essential data analysis tools for answering complex big data questions based on real world dataContains machine learning estimators that provide inference within data science&nbspOffers applications that .…

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 684 pp. Englisch.…

Buch. Condición: Neu. nach der Bestellung gedruckt 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.…

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Provides essential data analysis tools for answering complex big data questions based on real world dataSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 684 pp. Englisch.