Introducing LASSO-type penalisation to generalised joint regression modelling for count data

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2021-11-12 DOI:10.1007/s10182-021-00425-5

Hendrik van der Wurp, Andreas Groll

引用次数: 1

Abstract

In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the R package GJRM by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data.

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在计数数据的广义联合回归模型中引入lasso型惩罚

在这项工作中，我们提出了Marra和Radice (R包版本0.2-3,2020)对R包GJRM的双变量计数响应的通用联合回归框架的扩展，通过纳入(自适应)lasso型惩罚。底层估计算法基于惩罚的二次逼近。该方法允许变量选择，相应的估计保证收缩和稀疏性。因此，这种方法在高维计数响应设置中特别有用。通过仿真研究和对世界杯足球数据的应用，验证了该方法的实证效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

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