Kumaraswamy regression modeling for Bounded Outcome Scores

In this paper, we use a regression model for modeling bounded outcome scores (BOS), where the outcome is Kumaraswamy distributed. Similar to the Beta distribution, this distribution can take a variety of shapes while being computationally easier to use. Thus, it is deemed as a suitable alternative distribution to the Beta in modeling bounded random processes. In the proposed model, the median of a bounded response is modeled by the linear predictors which are defined through regression parameters and explanatory variables. We obtained the maximum likelihood estimates (MLEs) of the parameters, provided closed-form expressions for the score functions and Fisher information matrix, and presented some diagnostic measures. We conducted Monte Carlo simulations to investigate the finite-sample performance of the MLEs of the parameters. Finally, two practical applications of this model to the real data sets are presented and discussed.

Automated summary

In this study, a regression model was used to model bounded outcome scores (BOS) following the Kumaraswamy distribution. The model estimated maximum likelihood estimates (MLEs) of parameters and conducted Monte Carlo simulations to investigate the finite-sample performance. Two practical applications of the model to real data sets were also presented and discussed.