國立虎尾科技大學 |

Targeted learning in data science = causal inference for complex longitudinal studies /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Targeted learning in data science/ by Mark J. van der Laan, Sherri Rose.
Reminder of title:	causal inference for complex longitudinal studies /
Author:	Laan, Mark J. van der.
other author:	Rose, Sherri.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xlii, 640 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-65304-4
ISBN:	9783319653044

Targeted learning in data science = causal inference for complex longitudinal studies /
Laan, Mark J. van der.

Targeted learning in data sciencecausal inference for complex longitudinal studies /[electronic resource] :by Mark J. van der Laan, Sherri Rose. - Cham :Springer International Publishing :2018. - xlii, 640 p. :ill., digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

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.

ISBN: 9783319653044

Standard No.: 10.1007/978-3-319-65304-4doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .L336 2018

Dewey Class. No.: 006.31

Targeted learning in data science = causal inference for complex longitudinal studies /
LDR:03233nam a2200313 a 4500 001 925205
003 DE-He213
005 20181011112253.0
006 m d
007 cr nn 008maaau
008 190625s2018 gw s 0 eng d
020 $a 9783319653044 $q (electronic bk.)
020 $a 9783319653037 $q (paper)
024 7 $a 10.1007/978-3-319-65304-4 $2 doi
035 $a 978-3-319-65304-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .L336 2018
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .L111 2018
100 1 $a Laan, Mark J. van der. $3 1202745
245 1 0 $a Targeted learning in data science $h [electronic resource] : $b causal inference for complex longitudinal studies / $c by Mark J. van der Laan, Sherri Rose.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xlii, 640 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 0172-7397
520 $a 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.
650 0 $a Machine learning. $3 561253
650 0 $a Mathematical statistics. $3 527941
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Big Data/Analytics. $3 1106909
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Biostatistics. $3 783654
650 2 4 $a Biomedical Engineering/Biotechnology. $3 1068811
650 2 4 $a Public Health. $3 592982
700 1 $a Rose, Sherri. $3 787460
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 882180
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-65304-4
950 $a Mathematics and Statistics (Springer-11649)