Structured Additive Distributional Regression

回归模型是所有科学学科，特别是经济学中实证分析的标准工具之一。虽然通常的回归规范，如线性模型或广义线性模型的目的是描述响应条件的协变量的期望，最近的兴趣已经转移到模型，允许分析更一般的属性的分布的响应（我们将在下文中称为分布回归）。这包括完全分布自由的方法，如分位数和期望回归，以及灵活的参数方法的位置，规模和形状。复杂的协变量信息的日益增加的可用性，也诱导了类似的复杂的预测规范，如非线性和空间效应的背景下，分布registration.This项目扩展了不同类别的分布registration.This和开发相应的推理技术的需求。考虑的模型类包括不同版本的分位数和期望回归，模态回归和回归模型的位置，规模和形状。此外，开发的方法将在不同的应用领域进行实证分析。

Regression models form one of the standard tools for empirical analyses in all scientific disciplines and in particular in economics. While usual regression specifications such as the linear model or generalized linear models aim at describing the expectation of a response conditional on covariates, recent interest has shifted towards models that allow to analyse more general properties of the distribution of a response (we will refer to this as distributional regression in the following). This comprises completely distribution free approaches such as quantile and expectile regression as well as flexible parametric approaches for location, scale and shape. The increasing availability of complex covariate information also induces a requirement for similarly complex predictor specifications such as nonlinear and spatial effects in the context of distributional regression.This project extends different classes of distributional regression and develops corresponding inferential techniques. The model classes considered comprise different versions of quantile and expectile regression, modal regression and regression models for location, scale and shape. The developed methods will furthermore be employed in the different areas of application for empirical analyses.