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.

翻译：以分布的对称组合为基础的回归模型在反应绝对正对和不对称时特别有用。 在本文中,我们建议了一组基于重计对称分布的四分位回归模型,这些模型有四分位参数。两个蒙特卡洛模拟研究是使用R软件进行的。第一个模型分析了最大概率估计器的性能、信息标准AIC、BIC和AICc,以及通用的Cox-Snell和随机定量残留物。第二个模型评估了Wald的大小和功率、概率比率、分数和梯度测试的性能。最终分析了一个真正的盒式办公室数据集,以说明拟议方法。