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Bayesian Analysis of Least Absolute Relative Error Regression

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  • Efthymios G. Tsionas

Abstract

In an important article by Chen et al. (2010) introduced a new distribution compatible with maximum likelihood estimation in a Least Absolute Relative Error (LARE) setting. In this article, we show first that the posterior of the model is log – concave and thus specialized and highly efficient techniques can be used to perform Bayesian inference without the use of MCMC since they provide independent draws from the posterior. Second, we approximate the distribution using a finite mixture of normals. Surprisingly, the log-LARE distribution can be approximated using a finite scale mixture of normals with few components.

Suggested Citation

  • Efthymios G. Tsionas, 2014. "Bayesian Analysis of Least Absolute Relative Error Regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(23), pages 4988-4997, December.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:23:p:4988-4997
    DOI: 10.1080/03610926.2012.738843
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