Additive Mixed Models for Location, Scale and Shape via Gradient Boosting Techniques

被引：0
|
作者：
Griesbach, Colin [1 ]
Bergherr, Elisabeth [1 ]
机构：
[1] Georg August Univ Gottingen, Gottingen, Germany
来源：
DEVELOPMENTS IN STATISTICAL MODELLING, IWSM 2024 | 2024年
关键词：
Statistical Boosting; Distributional Regression; Random Effects;
D O I：
10.1007/978-3-031-65723-8_34
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this work we adapt recent findings from statistical boosting in order to construct an estimation approach for distributional regression including random effects. The algorithm is applied to registry data provided by the German Cystic Fibrosis Registry where the subject-specific evolution of each patients lung function and its corresponding distributional parameters are modelled.
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页码：218 / 223
页数：6
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