The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution such as bimodality, real moments, entropy measures and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality.
翻译:贝塔模型是使用单位间隔进行数据匹配的最重要分布方式。 但是, 贝塔分布不适合模拟双模单位间隔数据。 在本文中, 我们建议采用基于 alpha-skew- 正常模型的方法构建双模贝塔分布。 我们讨论了这种分布的若干特性, 如双模、 真实瞬间、 恒温度和可识别性。 此外, 我们根据提议的模型提出一个新的回归模型, 并讨论残留物 。 估计以最大可能性进行 。 进行蒙特卡洛 实验, 评估这些定点样本中测算员的性能, 并讨论结果 。 当数据集显示双模时, 应用来显示拟议分布的建模能力 。