having the normal distribution as a limit case when $\delta \rightarrow\infty$. The behaviour of the tails of the Student-$t$ density is polynomial. On the other hand, the symmetric sinh-arcsinh density (reported in Appendix A) is a parametric density function which contains a parameter that controls the tail behaviour. This distribution can capture tails heavier or lighter than those of the normal density ($\delta\lessgtr 1$), being the normal distribution a particular case ($\delta=1$). The tails of the symmetric sinh-arcsinh density are lighter than any polynomial [@JP09]. Therefore, with these two choices of the baseline density we can cover a wide range of tail behaviours. Moreover, with the additional shape parameter $\gamma$ we also cover a wide range of shapes around the shoulders of the density. Table \[table:NerveMLE\] shows the MLEs and the Akaike information criterion (AIC) associated to these models as well as some natural competitors. We also report the estimators of the LTP Normal and the lognormal distributions, which are particular cases of the LTP SAS model. The AIC favours the LTP SAS model overall, closely followed by the LTP Normal. The MLE of the parameter $\delta$ in the LTP SAS model is larger than one, indicating that the data 