A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models.

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression () model assuming errors with two-piece scale mixtures of normal (-) distributions. The - is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for modelling in different practical settings.