Jetzt sparen: 40% auf Finanzierung & Banken, Mathematik und Statistik oder ausgewählte englischsprachige Sachbücher!

Springer Series in Statistics

Targeted Learning in Data Science

Causal Inference for Complex Longitudinal Studies

Autoren: van der Laan, Mark J., Rose, Sherri

Vorschau
  • Provides essential data analysis tools for answering complex big data questions based on real world data
  • Contains machine learning estimators that provide inference within data science 
  • Offers applications that demonstrate 1) the translation of the real world application into a statistical estimation problem and 2) the targeted statistical learning methodology to answer scientific questions of interest based on real data
Weitere Vorteile

Dieses Buch kaufen

eBook 74,89 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-65304-4
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF, EPUB
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 96,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-65303-7
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
Softcover 96,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-030-09736-3
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
Über dieses Lehrbuch

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.

Über den Autor

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. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. 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 or 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 has made major contributions to the development and application of targeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. 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

Stimmen zum Buch

“A list of abbreviations, including all the statistical terms used in the textbook, as well as a list of tables and figures would be a welcome addition to the book. This may be particularly useful as the TMLE is a very important application in parametric statistics, and may be used by biostatisticians … . Specifically, those with a very good knowledge of advanced theoretical statistics, including the observational and modeling statistics that are almost prerequisite for appreciating this textbook.” (Ramzi El Feghali, ISCB News, iscb.info, Issue 67, June, 2019)


“The book recommends itself as a thorough overview of TMLE approaches with a variety of examples and case studies, all presented in detail, in a text-book like manner, making this work accessible to a wide audience from undergraduates to established researchers.” (Irina Ioana Mohorianu, zbMATH 1408.62005, 2019)

Inhaltsverzeichnis (30 Kapitel)

Inhaltsverzeichnis (30 Kapitel)

Dieses Buch kaufen

eBook 74,89 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-65304-4
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF, EPUB
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 96,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-65303-7
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
Softcover 96,29 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-030-09736-3
  • Kostenfreier Versand für Individualkunden weltweit
  • Gewöhnlich versandfertig in 3-5 Werktagen.
Loading...

Wir empfehlen

Loading...

Bibliografische Information

Bibliographic Information
Buchtitel
Targeted Learning in Data Science
Buchuntertitel
Causal Inference for Complex Longitudinal Studies
Autoren
Titel der Buchreihe
Springer Series in Statistics
Copyright
2018
Verlag
Springer International Publishing
Copyright Inhaber
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-65304-4
DOI
10.1007/978-3-319-65304-4
Hardcover ISBN
978-3-319-65303-7
Softcover ISBN
978-3-030-09736-3
Buchreihen ISSN
0172-7397
Auflage
1
Seitenzahl
XLII, 640
Anzahl der Bilder
37 schwarz-weiß Abbildungen
Themen