A class of asymmetric regression models for left-censored data

A common assumption in the standard tobit model is the normality for the error distribution. However, asymmetry and bimodality may be present and alternative tobit models must be used in such cases. In this paper, we propose a tobit model based on the class of log-symmetric distributions, which includes as special cases heavy/light tailed distributions and bimodal distributions. We implement a likelihood-based approach for parameter estimation …