logit, unit interval, bounded data, beta regression, quantile regression, simulation study
In this paper, we recommend a mechanism for determining whether to logit or not to logit data in the unit interval which is based on quantile estimation of data between 0 and 1. By using a simulated dataset generated from a Beta regression model, the estimated quantile for this model perform better than those based on the linear quantile regression with logit transformation.
Further, we investigate the performance of the quantile regression estimators based on the LQR and we conclude that it is better than those based on the Beta regression when the distribution is contaminated with 10% uniform numbers between 0 and 1. The proposed recommendation is that we can use logit transformation LQR if (1) we are dealing with quantile estimation in data between 0 and 1 (2) we ascertain that the data fit well to the contemplated bounded data regressions (whether Beta Regression or otherwise) and (3) if the fit of the model is suspected.
Master of Science
Mathematics and Statistics
Major Research Paper