Distribution-based Regression Models for Semi-Bounded Data Analysis

Positive vectors appear naturally in many applications, yet heir modeling has not been adequately addressed in the past. Inverted Dirichlet offers a good representation and modeling of positive non-Gaussian vectors, and its generalizations offer even more practical and flexible alternatives. In this paper, we propose three regression models based on flexible distributions for semi-bounded data, namely, inverted Dirichlet, inverted generalize Dirichlet, and inverted Beta-Liouville. The efficiency of these models is tested via real-world applications, including software defects prediction, age estimation, spam filtering, and disease diagnosis. Our results show that the three proposed regression models outperform other commonly used regression approaches.