LASSO-type penalization in the framework of generalized additive models for location, scale and shape
LASSO-type penalization in the framework of generalized additive models for location, scale and shape
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DOI：
10.1016/j.csda.2019.06.005
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发表时间：
2019-12-01
影响因子：
1.8
通讯作者：
Umlauf, Nikolaus
中科院分区：
文献类型：
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作者：
Groll, Andreas;Hambuckers, Julien;Umlauf, Nikolaus
对于许多应用，提供完全的概率预测是有意义的，它能够为每个预测结果分配概率。因此，注意力不断从条件均值模型转移到概率分布模型，以捕捉响应分布的位置、规模、形状和其他方面。分布回归的一个最成熟的模型是位置、尺度和形状的广义加性模型（GAMLSS）。在高维数据的设置，经典的拟合过程中，GAMLSS往往变得相当不稳定，变量选择的方法是可取的。因此，提出了一种在GAMLSS框架下的高维数据集正则化方法。它是为线性协变量效应设计的，基于L-1型惩罚。提供了以下三种惩罚选项：用于度量协变量的传统最小绝对收缩和选择算子（LASSO），以及用于分类预测因子的分组和融合LASSO。针对模拟数据和两个真实的数据示例（即慕尼黑租金数据和意大利联合信贷银行的极端运营损失数据）对这些方法进行了研究。(C)2019 Elsevier B. V.版权所有。
For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high-dimensional data set-ups, classical fitting procedures for GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, a regularization approach for high-dimensional data set-ups in the framework of GAMLSS is proposed. It is designed for linear covariate effects and is based on L-1-type penalties. The following three penalization options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data examples, namely Munich rent data and data on extreme operational losses from the Italian bank UniCredit. (C) 2019 Elsevier B.V. All rights reserved.