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

2023/06/14 by Luca Bagnato, Bagnato, Luca, Alessio Farcomeni +3 · 1 citation
Computer Science · Mathematics · #62F07 #62F30 #62H10 #62J07 #Advanced Statistical Methods and Models #Applications (stat.AP) #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #G.3 #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2306.08692

openalex publication_date 2023/06/14 · openalex created_date 2023/06/17 · openalex updated_date 2026/07/28

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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