Goodness of Fit Testing for the Log-logistic Distribution Based on Type I Censored Data

Abstract

A goodness of fit test procedure is proposed for the log-logistic distribution when the available data are subject to Type I censoring. The proposed test is based on transforming type 1 censored data into complete data from a suitably truncated distribution. A Monte Carlo power study is conducted to evaluate and compare the performance of the proposed method with the existing classical methods. An application based on a real dataset is considered for illustrative purposes