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.

翻译：我们重新审视了广义双曲（GH）分布及其嵌套模型。这些模型包括多元正态分布、偏t分布、拉普拉斯分布等广泛使用的参数化选择。我们同时引入了多选LASSO，一种针对同一参数的不同约束进行选择的新型惩罚方法。通过优化层次化的多选LASSO惩罚似然函数，可在GH族内实现同步模型选择与推断。我们通过仿真研究展示了该方法的应用。本文提出的方法已通过R函数实现，并作为补充材料提供。