The flexible modeling of an entire distribution as a function of covariates, known as distributional regression, has seen growing interest over the past decades in both the statistics and machine learning literature. This review outlines selected state-of-the-art statistical approaches to distributional regression, complemented with alternatives from machine learning. Topics covered include the similarities and differences between these approaches, extensions, properties and limitations, estimation procedures, and the availability of software. In view of the increasing complexity and availability of large-scale data, this review also discusses the scalability of traditional estimation methods, current trends, and open challenges. Illustrations are provided using data on childhood malnutrition in Nigeria and Australian electricity prices.

中文翻译：

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用于数据分析的分布回归


过去几十年来，统计学和机器学习文献中越来越关注将整个分布作为协变量函数的灵活建模，称为分布回归。这篇综述概述了精选的最先进的分布回归统计方法，并辅以机器学习的替代方案。涵盖的主题包括这些方法之间的异同、扩展、属性和限制、估计程序以及软件的可用性。鉴于大规模数据的复杂性和可用性日益增加，本文还讨论了传统估计方法的可扩展性、当前趋势和面临的挑战。使用尼日利亚儿童营养不良和澳大利亚电价的数据提供了插图。