Applied Regression Analysis
An introduction to regression analysis and statistical learning with an emphasis on mathematical understanding and its software implementation. Programming uses Python.
The Elements of Statistical Learning, Hastie, Tibshirani and Friedman, Springer, corrected 12th printing, 2017.
- Linear regression: parameter estimation, confidence ellipsoids and prediction intervals, hypothesis tests.
- Classification: logistic regression, linear discriminant analysis.
- Basis expansion: polynomial regression, regression splines.
- Resampling methods: cross-validation, bootstrap.
- Shrinkage methods.
- Model selection: information criteria, forward and backward selection, lasso.
- Decision trees and random forests - bagging, boosting.
(Talata 2020 )