Bayesian composite quantile regression for the single-index model

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Mont...

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Detalles Bibliográficos
Autores principales: Yuan, Xiaohui, Xiang, Xuefei, Zhang, Xinran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171657/
https://www.ncbi.nlm.nih.gov/pubmed/37163496
http://dx.doi.org/10.1371/journal.pone.0285277
Descripción
Sumario:By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset.

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