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

Samah Ahmed, A. Baklizi, Reza Pakyari
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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
基于I型截尾数据的logistic分布拟合优度检验
提出了一种对可得数据进行I型审查时的对数-logistic分布的拟合优度检验方法。所提出的测试是基于将1型截尾数据转换为来自适当截尾分布的完整数据。通过蒙特卡洛功率研究,对该方法与现有经典方法的性能进行了评价和比较。为了便于说明,考虑了一个基于真实数据集的应用程序
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