We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves, variances and other distributional parameters of the response in dependence of various scalar and functional covariate effects. In addition, the scope of distributions is extended beyond exponential families. The model is fitted via gradient boosting, which offers inherent model selection and is shown to be suitable for both complex model structures and highly auto-correlated response curves. This enables us to analyze bacterial growth in \textit{Escherichia coli} in a complex interaction scenario, fruitfully extending usual growth models.

中文翻译：

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通过应用于细菌竞争来提升位置、规模和形状的功能响应模型

我们将位置、尺度和形状的广义加性模型 (GAMLSS) 扩展到具有功能响应的回归。这使我们能够根据各种标量和函数协变量效应，同时对响应的逐点平均曲线、方差和其他分布参数进行建模。此外，分布的范围超出了指数族。该模型通过梯度提升拟合，它提供了固有的模型选择，并被证明适用于复杂的模型结构和高度自相关的响应曲线。这使我们能够在复杂的交互场景中分析 \textit{Escherichia coli} 中的细菌生长，从而富有成效地扩展了通常的生长模型。