Falls Sie nur an einem bestimmten Exempar interessiert sind, können Sie aus der folgenden Liste jenes wählen, an dem Sie interessiert sind:
Nur diese Ausgabe anzeigen…
Nur diese Ausgabe anzeigen…
Targeted Learning in Data Science - 14 Angebote vergleichen
Preise | 2017 | 2019 | 2020 |
---|---|---|---|
Schnitt | € 68,36 | € 62,33 | € 77,62 |
Nachfrage |
Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies (2011)
ISBN: 9783319653044 bzw. 3319653040, vermutlich in Englisch, Springer International Publishing, neu, E-Book, elektronischer Download.
Targeted Learning in Data Science (2011)
ISBN: 9783319653044 bzw. 3319653040, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics. eBook.
Targeted Learning in Data Science (2011)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Deutsch, Springer-Verlag GmbH, neu.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose`s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
Targeted Learning in Data Science (2011)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Englisch, Springer Shop, gebundenes Buch, neu.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics. Hard cover.
Targeted Learning In Data Science: Causal Inference For Complex Longitudinal Studies (2011)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Englisch, Springer Nature, neu.
Mark J. van der Laan, Sherri Rose, Books, Science and Nature, Targeted Learning In Data Science: Causal Inference For Complex Longitudinal Studies, This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included inTargeted Learning in DataScienceare demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference,Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for theJournal of the American Statistical Association and Biostatistics.
Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies (2011)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Deutsch, Springer-Verlag Gmbh, gebundenes Buch, neu.
Targeted Learning in Data Science - Causal Inference for Complex Longitudinal Studies (2011)
ISBN: 9783319653044 bzw. 3319653040, vermutlich in Englisch, Springer International Publishing, neu, E-Book, elektronischer Download.
Targeted Learning in Data Science: This textbook for graduate students in statistics, data science, and public health deals  with the practical challenges that come with big, complex, and dynamic data. It presents  a scientific roadmap to translate real-world data science applications into formal statistical  estimation problems by using the general template of targeted maximum likelihood  estimators. These targeted machine learning algorithms estimate quantities of interest  while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques  can answer complex questions including optimal rules for assigning treatment based  on longitudinal data with time-dependent confounding, as well as other estimands in  dependent data structures, such as networks. Included in Targeted Learning in Data  Science are demonstrations with soft ware packages and real data sets that present a  case that targeted learning is crucial for the next generation of statisticians and data  scientists. Th is book is a sequel to the first textbook on machine learning for causal  inference, Targeted Learning, published in 2011.Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and  Statistics at UC Berkeley. His research interests include statistical methods in genomics,  survival analysis, censored data, machine learning, semiparametric models, causal  inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman  Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005  COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics  and statistics.Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard  Medical School. Her work is centered on developing and integrating innovative statistical  approaches to advance human health. Dr. Rose`s methodological research focuses  on nonparametric machine learning for causal inference and prediction. She co-leads  the Health Policy Data Science Lab and currently serves as an associate editor for the  Journal of the American Statistical Association and Biostatistics. Englisch, Ebook.
Targeted Learning in Data Science (2018)
ISBN: 9783319653044 bzw. 3319653040, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science application.
Targeted Learning in Data Science (2018)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Englisch, Springer International Publishing Apr 2018, gebundenes Buch, neu, Nachdruck.
Von Händler/Antiquariat, BuchWeltWeit Inh. Ludwig Meier e.K. [57449362], Bergisch Gladbach, Germany.
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 integrating innovative 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, Books.
Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies (2018)
ISBN: 9783319653037 bzw. 3319653032, vermutlich in Englisch, Springer International Publishing, gebundenes Buch, neu.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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 integrating innovative 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, Books.