1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.

These notes aim at giving a broad skill set to the actuarial profession in insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding....

In this paper, we derive a rate of convergence of the Lasso estimator when the penalty parameter Lambda for the estimator is chosen using K-fold cross-validation; in particular, we show that in the model with Gaussian noise and under fairly general assumptions on the candidate set of values of...