Statistical boosting algorithms are renowned for their intrinsic variable selection and enhanced predictive performance compared to classical statistical methods, making them especially useful for complex models such as generalized additive models for location scale and shape (GAMLSS). Boosting this model class can suffer from imbalanced updates across the distribution parameters as well as long computation times. Shrunk optimal step lengths have been shown to address these issues. To examine the influence of socio-economic factors on the distribution of the number of antenatal care visits in Nigeria, we generalize boosting of GAMLSS with shrunk optimal step lengths to base-learners beyond simple linear models and to a more complex response variable distribution. In an extensive simulation study and in the application we demonstrate that shrunk optimal step lengths yield a more balanced regularization of the overall model and enhance computational efficiency across diverse settings, in particular in the presence of base-learners penalizing the size of the fit.

翻译：相较于经典统计方法，统计提升算法以其固有的变量选择和更强的预测性能而著称，尤其适用于复杂模型，如位置、尺度与形状的广义可加模型（GAMLSS）。提升此类模型时，可能会面临分布参数间更新不平衡以及计算时间过长的问题。收缩最优步长已被证明能有效解决这些问题。为研究社会经济因素对尼日利亚产前护理就诊次数分布的影响，我们将基于收缩最优步长的GAMLSS提升方法推广至超越简单线性模型的基学习器，并应用于更复杂的响应变量分布。通过广泛的模拟研究和实际应用，我们证明收缩最优步长能对整体模型实现更均衡的正则化，并在不同设置下（尤其是在使用对拟合规模进行惩罚的基学习器时）显著提升计算效率。