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.
Bagnato, L., Farcomeni, A., Punzo, A., The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO, <<STATISTICAL ANALYSIS AND DATA MINING>>, 2024; 17 (1): 1-13. [doi:10.1002/sam.11652] [https://hdl.handle.net/10807/277396]
The generalized hyperbolic family and automatic model selection through the multiple-choice LASSO
Bagnato, Luca;
2024
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.