广义双曲线族和通过多选 LASSO 自动选择模型

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2023-12-08 DOI:10.1002/sam.11652

Luca Bagnato, Alessio Farcomeni, Antonio Punzo

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引用次数: 0

摘要

我们重温了广义双曲线（GH）分布及其嵌套模型。这些模型包括广泛使用的参数选择，如多元正态分布、偏斜-t$$ t $$分布、拉普拉斯分布以及其他一些参数。我们还介绍了多选 LASSO，这是一种在同一参数的备选约束条件中进行选择的新型惩罚性方法。我们优化了分层多选最小绝对收缩和选择操作符（LASSO）惩罚似然法，以便在 GH 系列中同时执行模型选择和推断。我们通过模拟研究和真实数据示例来说明我们的方法。本文提出的方法已在 R 函数中实现，这些函数可作为补充材料提供。

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

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.

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.