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

Luca Bagnato, Alessio Farcomeni, Antonio Punzo*

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

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 Least Absolute Shrinkage and Selection Operator (LASSO) penalized likelihood is optimized to perform simultaneous model selection and inference within the GH family. We illustrate our approach through a simulation study and a real data example. The methodology proposed in this paper has been implemented in R functions which are available as supplementary material.
Lingua originaleEnglish
pagine (da-a)1-13
Numero di pagine13
RivistaStatistical Analysis and Data Mining
Volume17
DOI
Stato di pubblicazionePubblicato - 2024

Keywords

  • EM algorithm
  • generalized hyperbolic distribution
  • kurtosis
  • penalized likelihood
  • skewness

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