Statistical Foundations of Data Science  PDF & ePUB Download
PDF
 eBook:Statistical Foundations of Data Science
 Author:Jianqing Fan, Runze Li, CunHui Zhang, Hui Zou
 Edition:1 edition
 Categories:
 Data:August 17, 2020
 ISBN:1466510846
 ISBN13:9781466510845
 Language:English
 Pages:774 pages
 Format:PDF
Description of Statistical Foundations of Data Science ebook
The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. Highdimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on highdimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Content
2. Multiple and Nonparametric Regression
3. Introduction to Penalized LeastSquares
4. Penalized Least Squares: Properties
5. Generalized Linear Models and Penalized Likelihood
6. Penalized Mestimators
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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