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In previous studies on fitting non-linear regression models with the symmetric structure the normality is usually assumed in the analysis of data. This choice may be inappropriate when the distribution of residual terms is asymmetric. Recently, the family of scale-mixture of skew-normal distributions is the main concern of many researchers. This family includes several skewed and heavy-tailed distributions, such as the skew-t and the skew slash, as special cases and is recommended as an alternative to the normal distribution. The statistical inference based on the maximization of marginal likelihoods is complicated, in general, for non-linear regression models and thus we implement the MCMC approach to obtain Bayes estimates. Finally, we fit a non-linear regression model using proposed distributions for a real data set to show the importance of the recommended model.

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In many issues of statistical modeling, the common assumption is that observations are normally distributed. In
many real data applications, however, the true distribution is deviated from the normal. Thus, the main concern of
most recent studies on analyzing data is to construct and the use of alternative distributions. In this regard, new
classes of distributions such as slash and skew-slash family of distributions have been introduced .This has been the
main concern of many researcher’s investigations in recent decades. Slash distribution, as a heavy tailed symmetric
distribution, is known in robust studies. But since , in empirical examples, there are many situations where symmetric
distributions are not suitable for fitting the data study of skew distributions has become of particular importance.In
this paper we introduce skew-slash distribution and study their properties. Finally, some applications to several real
data sets are illustrated in order to show the importance of the distribution in regression models.