Statistical Foundations of Data Science (Hardcover)

Introduction. Linear Regression Models. Nonparametric Techniques. Penalized Least Squares: Methods and Algorithms. High-Dimensional Penalized Least Squares: Theories and Properties. Compressed Sensing. Generalized Linear Models and Quasi-Likelihood. Penalized Likelihood. Feature Screening. Classification. Classification of Objects. Clustering and PCA. Matrix Estimation and Graphical Models. Survival Data Analysis. Quantile and Robust Regression. Nonparametric and Semiparametric Models.