The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO
Methodology
2023-07-13 v2 Applications
Computation
Abstract
We revisit the generalized hyperbolic (GH) distribution and its nested models. These include widely used parametric choices like the multivariate normal, skew-t, Laplace, and several others. We also introduce the multiple-choice LASSO, a novel penalized method for choosing among alternative constraints on the same parameter. A hierarchical multiple-choice LASSO penalized likelihood is optimized to perform simultaneous model selection and inference within the GH family. We illustrate our approach through a simulation study. The methodology proposed in this paper has been implemented in R functions which are available as supplementary material.
Cite
@article{arxiv.2306.08692,
title = {The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO},
author = {Luca Bagnato and Alessio Farcomeni and Antonio Punzo},
journal= {arXiv preprint arXiv:2306.08692},
year = {2023}
}