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However, several formulations of this class make either restrictive modelling assumptions or involve intricate algorithms for posterior inference. We propose a flexible and computationally convenient approach for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses.  We assume an additive structure for the mean of each mixture component and incorporate the effects of continuous covariates through smooth functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. Our method also seamlessly accommodates categorical covariates, linear effects of continuous covariates, varying coefficient terms, and random effects, which is why we refer our model as a Dirichlet process mixture of normal structured additive regression models. A notable feature of our method is the simplicity of posterior simulation using Gibbs sampling, as closed-form full conditional distributions for all model parameters are available. Results from a simulation study demonstrate that our approach successfully recovers the true conditional densities and other regression functionals in challenging scenarios. Applications to three real datasets further underpin the broad applicability of our method. An  package, , implementing the proposed method is provided.<\/jats:p>","DOI":"10.1007\/s11222-025-10567-0","type":"journal-article","created":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T01:01:46Z","timestamp":1739062906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Density regression via Dirichlet process mixtures of normal structured additive regression models"],"prefix":"10.1007","volume":"35","author":[{"given":"Mar\u00eda Xos\u00e9","family":"Rodr\u00edguez-\u00c1lvarez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanda","family":"In\u00e1cio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadja","family":"Klein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"issue":"506","key":"10567_CR1","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1080\/01621459.2013.879061","volume":"109","author":"I Antoniano-Villalobos","year":"2014","unstructured":"Antoniano-Villalobos, I., Wade, S., Walker, S.G.: A Bayesian nonparametric regression model with normalized weights: a study of hippocampal atrophy in Alzheimer\u2019s disease. 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