Jianqing Fan (Princeton University, New Jersey, USA), Runze Li (Pennsylvania State University, University Park, USA), Cun-Hui Zhang (Rutgers University, Piscataway, USA)

Statistical Foundations of Data Science

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Chapman & Hall/CRC Data Science Series
• Verlag: Taylor & Francis Inc
• Seitenzahl: 774
• Erscheinungstermin: 17. August 2020
• Englisch
• Abmessung: 238mm x 161mm x 48mm
• Gewicht: 1378g
• ISBN-13: 9781466510845
• ISBN-10: 1466510846
• Artikelnr.: 60624260

Autorenporträt

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.

Inhaltsangabe

1. Introduction. 2. Multiple and Nonparametric Regression. 3. Introduction
to Penalized Least-Squares. 4. Penalized Least Squares: Properties. 5.
Generalized Linear Models and Penalized Likelihood. 6. Penalized
M-estimators. 7. High Dimensional Inference 8. Feature Screening. 9.
Covariance Regularization and Graphical Models. 10. Covariance Learning and
Factor Models. 11. Applications of Factor Models and PCA. 12. Supervised
Learning. 13. Unsupervised Learning. 14. An Introduction to Deep Learning.