Bayesian Mode Regression

Summary

This paper introduces Bayesian mode regression by exploring three different approaches, starting from a parametric Bayesian model by employing a likelihood function that is based on a mode uniform distribution, a nonparametric Bayesian model by using Dirichlet process mixtures of mode uniform distributions and a Bayesian empirical likelihood mode regression by taking empirical likelihood into a Bayesian framework.

Keywords

Model selection, Markov chain Monte Carlo, Bayesian probability, Linear model, Bayesian inference

References

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