Regression models based on the log-symmetric family of distributions are
particularly useful when the response is strictly positive and asymmetric. In
this paper, we propose a class of quantile regression models based on
reparameterized log-symmetric distributions, which have a quantile parameter.
Two Monte Carlo simulation studies are carried out using the R software. The
first one analyzes the performance of the maximum likelihood estimators, the
information criteria AIC, BIC and AICc, and the generalized Cox-Snell and
random quantile residuals. The second one evaluates the performance of the size
and power of the Wald, likelihood ratio, score and gradient tests. A real box
office data set is finally analyzed to illustrate the proposed approach.