Robust Explicit Estimation of the Log-Logistic Distribution with Applications

The parameters of the log-logistic distribution are generally estimated based on classical methods such as maximum likelihood estimation, whereas these usually result in severe biased estimates when data contain outliers. In this paper, we consider several alternative estimators, which not only have closed-form expressions, but also quite robust to a certain level contamination. We investigate robustness property each estimator terms breakdown point. finite sample performance and effectiveness estimators evaluated through Monte Carlo simulations real-data application. Numerical results demonstrate that proposed perform favorably manner they comparable with for without contamination provide superior presence