Additive models are flexible regression tools that handle linear as well as non-linear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sampled according to cluster designs (e.g. longitudinal).These models find applications in the study of phenomena like growth, certain disease mechanisms and energy expenditure in humans, when repeated measurements are available. We propose a novel additive mixed model for quantile regression. Our methods are motivated by an application to physical activity based on a data set with more than half a million accelerometer measurements in children of the UK Millennium Cohort Study. In a simulation study, we assess the proposed methods against existing alternatives.

中文翻译：

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聚类数据的加性分位数回归及其在儿童身体活动中的应用。

加法模型是灵活的回归工具，可以处理线性和非线性项。后者通常通过平滑样条进行建模。当根据聚类设计（例如纵向）对数据进行采样时，加性混合模型扩展了加性模型以包括随机项。这些模型可用于研究诸如增长，某些疾病机制和人类的能量消耗等现象，只要可以进行重复测量。我们提出了一种新的加性混合模型用于分位数回归。我们的方法是根据对英国千禧年队列研究的儿童进行的超过一百万次加速度计测量的数据集应用到体育锻炼中来激发的。在模拟研究中，我们针对现有替代方案评估了所提出的方法。