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Distributional Regression for Data Analysis
Flexible modeling of how an entire distribution changes with covariates is an
important yet challenging generalization of mean-based regression that 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.Comment: Accepted for publication in Annual Review of Statistics and its
Applicatio
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