Statistical foundation of data science
著者
書誌事項
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.
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注記
Other authors: Runze Li, Cun-Hui Zhang, Hui Zou, Paula Moraga
Includes bibliographical references (p. 683-729) and index
内容説明・目次
内容説明
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
目次
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.
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