國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Targeted learning in data sciencecau...

Laan, Mark J. van der.

Targeted learning in data sciencecausal inference for complex longitudinal studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Targeted learning in data scienceby Mark J. van der Laan, Sherri Rose.
其他題名:	causal inference for complex longitudinal studies /
作者:	Laan, Mark J. van der.
其他作者:	Rose, Sherri.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xlii, 640 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	http://dx.doi.org/10.1007/978-3-319-65304-4
ISBN:	9783319653044$q(electronic bk.)

Targeted learning in data sciencecausal 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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

Targeted learning in data sciencecausal inference for complex longitudinal studies /
LDR:03233nmm a2200313 a 4500 001 533964
003 DE-He213
005 20181011112253.0
006 m d
007 cr nn 008maaau
008 181205s2018 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 809954
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 : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
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 188639
650 0 $a Mathematical statistics. $3 181877
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Big Data/Analytics. $3 742047
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Biostatistics. $3 339693
650 2 4 $a Biomedical Engineering/Biotechnology. $3 730838
650 2 4 $a Public Health. $3 320661
700 1 $a Rose, Sherri. $3 515965
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 280826
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-65304-4
950 $a Mathematics and Statistics (Springer-11649)