February 2022 The Dependent Dirichlet Process and Related Models
Fernando A. Quintana, Peter Müller, Alejandro Jara, Steven N. MacEachern
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Statist. Sci. 37(1): 24-41 (February 2022). DOI: 10.1214/20-STS819


Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not always appropriate to assume a location/scale representation, where the error distribution has unchanging shape over the predictor space. In fact, it often happens in applied research that the distribution of responses under study changes with predictors in ways that cannot be reasonably represented by a finite dimensional functional form. This can seriously affect the answers to the scientific questions of interest, and therefore more general approaches are indeed needed. This gives rise to the study of fully nonparametric regression models. We review some of the main Bayesian approaches that have been employed to define probability models where the complete response distribution may vary flexibly with predictors. We focus on developments based on modifications of the Dirichlet process, historically termed dependent Dirichlet processes, and some of the extensions that have been proposed to tackle this general problem using nonparametric approaches.


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Fernando A. Quintana. Peter Müller. Alejandro Jara. Steven N. MacEachern. "The Dependent Dirichlet Process and Related Models." Statist. Sci. 37 (1) 24 - 41, February 2022. https://doi.org/10.1214/20-STS819


Published: February 2022
First available in Project Euclid: 19 January 2022

MathSciNet: MR4371095
zbMATH: 07474196
Digital Object Identifier: 10.1214/20-STS819

Keywords: Bayesian nonparametrics , Nonparametric regression , Quantile regression , Related random probability distributions

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.37 • No. 1 • February 2022
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