Statistical Foundations of Data Science (PDF)
(Sprache: Englisch)
Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection,...
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Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management.
Autoren-Porträt von Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou
The authors are international authorities and leaders on the presented topics. All are fellows of the Institute of Mathematical Statistics and the American Statistical Association. Jianqing Fan is Frederick L. Moore Professor, Princeton University. He is co-editing Journal of Business and Economics Statistics and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, and Journal of Econometrics and has been recognized by the 2000 COPSS Presidents' Award, AAAS Fellow, Guggenheim Fellow, Guy medal in silver, Noether Senior Scholar Award, and Academician of Academia Sinica.
Runze Li is Elberly family chair professor and AAAS fellow, Pennsylvania State University, and was co-editor of The Annals of Statistics.
Cun-Hui Zhang is distinguished professor, Rutgers University and was co-editor of Statistical Science.
Hui Zou is professor, University of Minnesota and was action editor of Journal of Machine Learning Research.
Bibliographische Angaben
- Autoren: Jianqing Fan , Runze Li , Cun-Hui Zhang , Hui Zou
- 2020, 1. Auflage, 774 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1466510854
- ISBN-13: 9781466510852
- Erscheinungsdatum: 20.09.2020
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Größe: 33 MB
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Englisch
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