R Package

Sparse Regression with Multi-type Regularized Feature Modeling

We develop the SMuRF algorithm that accurately and effectively solves the general problem of convex optimization with a differentiable loss function and multi-type penalty terms. We show in full detail how this algorithm creates sparse models, using varying combinations of Lasso-type penalties, and investigating and documenting all possible model choices.

Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions

We develop a general procedure to fit a splicing model to censored and/or truncated data. We propose to combine the flexibility of the mixed Erlang distribution to model the body of the distribution with the Pareto distribution to provide a suitable fit for the tail.