Recursive nonparametric predictive for a discrete regression model

A recursive algorithm is proposed to estimate a set of distribution functions indexed by a regressor variable. The procedure is fully nonparametric and has a Bayesian motivation and interpretation. Indeed, the recursive algorithm follows a certain Bayesian update, defined by the predictive distribut...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 215; p. 108275
Main Authors:	Cappello, Lorenzo, Walker, Stephen G.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.03.2026
Subjects:	Bayesian nonparametrics Distribution regression Nonparametric density estimation Recursive algorithm Bayesian nonparametrics Nonparametric density estimation Distribution regression Recursive algorithm
ISSN:	0167-9473
Online Access:	Get full text
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Summary:	A recursive algorithm is proposed to estimate a set of distribution functions indexed by a regressor variable. The procedure is fully nonparametric and has a Bayesian motivation and interpretation. Indeed, the recursive algorithm follows a certain Bayesian update, defined by the predictive distribution of a Dirichlet process mixture of linear regression models. Consistency of the algorithm is demonstrated under mild assumptions, and numerical accuracy in finite samples is shown via simulations and real data examples. The algorithm is very fast to implement, it is parallelizable, sequential, and requires limited computing power.
ISSN:	0167-9473
DOI:	10.1016/j.csda.2025.108275