Springer Series in Statistics - Targeted Learning in Data Science Ebook Tooltip Ebooks kunnen worden gelezen op uw computer en op daarvoor geschikte e-readers. Causal Inference for Complex Longitudinal Studies
Afbeeldingen
Sla de afbeeldingen overArtikel vergelijken
- Engels
- E-book
- 9783319653044
- 28 maart 2018
- Adobe ePub
Samenvatting
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 integratinginnovative 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.
Productspecificaties
Inhoud
- Taal
- en
- Bindwijze
- E-book
- Oorspronkelijke releasedatum
- 28 maart 2018
- Ebook Formaat
- Adobe ePub
Betrokkenen
- Hoofdauteur
- Sherri Rose
- Tweede Auteur
- Mark J. Van Der Laan
- Hoofduitgeverij
- Springer
Lees mogelijkheden
- Lees dit ebook op
- Android (smartphone en tablet) | Kobo e-reader | Desktop (Mac en Windows) | iOS (smartphone en tablet) | Windows (smartphone en tablet)
Overige kenmerken
- Studieboek
- Nee
EAN
- EAN
- 9783319653044
Je vindt dit artikel in
- Categorieën
- Boek, ebook of luisterboek?
- Ebook
- Taal
- Engels
- Beschikbaarheid
- Leverbaar
- Studieboek of algemeen
- Studieboeken
Kies gewenste uitvoering
Prijsinformatie en bestellen
De prijs van dit product is 84 euro.- E-book is direct beschikbaar na aankoop
- E-books lezen is voordelig
- Dag en nacht klantenservice
- Veilig betalen
Rapporteer dit artikel
Je wilt melding doen van illegale inhoud over dit artikel:
- Ik wil melding doen als klant
- Ik wil melding doen als autoriteit of trusted flagger
- Ik wil melding doen als partner
- Ik wil melding doen als merkhouder
Geen klant, autoriteit, trusted flagger, merkhouder of partner? Gebruik dan onderstaande link om melding te doen.