Zero-Adjusted Log-Symmetric Quantile Regression Models

This paper proposes zero-adjusted log-symmetric quantile regressions to deal with the issue of regression estimation when there are many zeros in the dependent variable. We introduce the zero-adjusted log-symmetric distributions that accommodate the presence of zeros and are consistent with heteroscedasticity. The model builds on a conditional quantile distribution and the parameters are estimated by maximum likelihood. The quantile approach is …