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Abstract

Statistical models defined by shape constraints are a valuable alternative to parametric models or nonparametric models defined in terms of quantitative smoothness constraints. While the latter two classes of models are typically difficult to justify a priori, many applications involve natural shape constraints, for instance, monotonicity of a density or regression function. We review some of the history of this subject and recent developments, with special emphasis on algorithmic aspects, adaptivity, honest confidence bands for shape-constrained curves, and distributional regression, i.e., inference about the conditional distribution of a real-valued response given certain covariates.

Expected final online publication date for the , Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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/content/journals/10.1146/annurev-statistics-033021-014937
2023-10-13
2024-04-18
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  • Article Type: Review Article
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