Another unit Burr XII quantile regression model based on the different reparameterization applied to dropout in Brazilian undergraduate courses

• Published in: PloS one 2022-11, Vol.17 (11), p.e0276695-e0276695
• Main Authors: Ribeiro, Tatiane Fontana, Peña-Ramírez, Fernando A, Guerra, Renata Rojas, Cordeiro, Gauss M
• Format: Article
• Language: English
• Subjects: Animal sciences; College dropouts; Colleges & universities; Economic aspects; Learning; Maximum likelihood estimates; Maximum likelihood method; Methods; Modelling; Parameterization; Performance evaluation; Performance indices; Random variables; Regression analysis; Regression models; Skewness; Social aspects; Statistical analysis; Statistical learning (Psychology); Statistical methods; Statistics
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0276695

In many practical situations, there is an interest in modeling bounded random variables in the interval (0, 1), such as rates, proportions, and indexes. It is important to provide new continuous models to deal with the uncertainty involved by variables of this type. This paper proposes a new quantile regression model based on an alternative parameterization of the unit Burr XII (UBXII) distribution. For the UBXII distribution and its associated regression, we obtain score functions and observed information matrices. We use the maximum likelihood method to estimate the parameters of the regression model, and conduct a Monte Carlo study to evaluate the performance of its estimates in samples of finite size. Furthermore, we present general diagnostic analysis and model selection techniques for the regression model. We empirically show its importance and flexibility through an application to an actual data set, in which the dropout proportion of Brazilian undergraduate animal sciences courses is analyzed. We use a statistical learning method for comparing the proposed model with the beta, Kumaraswamy, and unit-Weibull regressions. The results show that the UBXII regression provides the best fit and the most accurate predictions. Therefore, it is a valuable alternative and competitive to the well-known regressions for modeling double-bounded variables in the unit interval.