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Annual Review of Statistics and Its Application

Volume 11, 2024

Distributional Regression for Data Analysis

Abstract

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.

Keyword(s): density regression, generalized additive model for location, probabilistic learning, quantile regression, scale and shape, transformation models
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/content/journals/10.1146/annurev-statistics-040722-053607
2024-04-22
2024-05-03
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/content/journals/10.1146/annurev-statistics-040722-053607
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Distributional Regression for Data Analysis
Annual Review of Statistics and Its Application 11, 321 (2024); https://doi.org/10.1146/annurev-statistics-040722-053607
/content/journals/10.1146/annurev-statistics-040722-053607
/content/journals/10.1146/annurev-statistics-040722-053607
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