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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… Click to show full 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.
Keywords:
regression;
single index;
composite quantile;
model
Journal Title: PLOS ONE
Year Published: 2023
Link to full text (if available)
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Journal Title: PLOS ONE
Year Published: 2023
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!