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. We examine the performance of the approach using simulated and real data.