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arXiv · 2306.08692

The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO

Luca Bagnato·Alessio Farcomeni·Antonio Punzo
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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.

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Luca Bagnato, Alessio Farcomeni, Antonio Punzo. 2023-06-14. The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO. https://arxiv.org/abs/2306.08692

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