We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
The generalized hyperbolic family and automatic model selection through the multiple‐choice LASSO.
- Authors
Bagnato, Luca; Farcomeni, Alessio; Punzo, Antonio
- Abstract
We revisit the generalized hyperbolic (GH) distribution and its nested models. These include widely used parametric choices like the multivariate normal, skew‐t$$ t $$, 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.
- Subjects
EXPECTATION-maximization algorithms; FAMILIES
- Publication
Statistical Analysis & Data Mining, 2024, Vol 17, Issue 1, p1
- ISSN
1932-1864
- Publication type
Article
- DOI
10.1002/sam.11652