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Distributional Regression for Data Analysis
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DOI：10.1146/annurev-statistics-040722-053607.png)
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Abstract
En 中文
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.
Keywords:
density regression
generalized additive model for location
scale and shape
probabilistic learning
quantile regression
transformation models
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8.7
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211
Citations:
2.4K
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