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Bayesian composite quantile regression for the single-index model.


ABSTRACT: 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.

SUBMITTER: Yuan X 

PROVIDER: S-EPMC10171657 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Bayesian composite quantile regression for the single-index model.

Yuan Xiaohui X   Xiang Xuefei X   Zhang Xinran X  

PloS one 20230510 5


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. ...[more]

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