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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Veröffentlicht in:Computational statistics & data analysis Jg. 215; S. 108275
Hauptverfasser: Cappello, Lorenzo, Walker, Stephen G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2026
Schlagworte:
Bayesian nonparametrics
Distribution regression
Nonparametric density estimation
Recursive algorithm
Bayesian nonparametrics
Nonparametric density estimation
Distribution regression
Recursive algorithm
ISSN:0167-9473
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Zusammenfassung: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