Statistical foundation of data science
Author(s)
Bibliographic Information
Statistical foundation of data science
（A Chapman & Hall book）（Chapman & Hall/CRC data science series）
CRC Press, an imprint of Taylor & Francis Group, 2020
- : hbk.
Available at 6 libraries
-
The Institute for Solid State Physics Library. The University of Tokyo.図書室
: hbk.417:S57210393513
Note
Other authors: Runze Li, Cun-Hui Zhang, Hui Zou, Paula Moraga
Includes bibliographical references (p. 683-729) and index
Description and Table of Contents
Description
Provides theoretical insights and justification of the statistical procedures for the analysis of high-dimensional data
Presents a general framework of regularization methods
Covers feature screening for ultrahigh-dimensional data
Describes large-scale covariance estimation
Table of Contents
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.
by "Nielsen BookData"