Inference on the Beta Type I Generalized Half Logistic Distribution under Right-Censored Observation with Application to COVID-19

Abstract

In real-life situations, censoring issues do arise due to the incompleteness of data. This article examined the inferences on right-censored beta type I generalized half logistic distribution. In this work, some statistical properties of the beta type I generalized half logistic distribution were derived. Furthermore, the beta type I generalized half logistic distribution was studied under a censoring situation in the presence and absence of covariates. Estimation of model parameters was conducted using the maximum likelihood estimation method. A simulation study was carried out to assess the performance of the parameters of the model in terms of efficiency and consistency. In a real-life application, the model was applied to COVID-19 data and the necessary inferences were drawn.