Modeling skew-symmetric distributions using B-spline and penalties

We propose a new flexible procedure for modeling skew-symmetric (SS) distributions via B-splines. To avoid over-fitting we follow a penalized likelihood estimation method. The structure of “B-splines SS with penalties” provides a flexible and smooth semi-parametric setting allowing estimates that capture many features of the target function such as asymmetry and multimodality. After outlining some theoretical results, we propose an effective computational strategy. Finally, we present some empirical results on both simulated and real data from chemical processing and banknote forgery data.