Estimating the Failure Rate of the Log-logistic Distribution by Smooth Adaptive and Bias-correction Methods

Abstract The Log-logistic distribution has successfully earned attention in practical applications due to its good statistical properties. Because the traditional maximum likelihood estimators of the Log-logistic distribution parameters do not have an explicit form and are biased when the sample size is small. Therefore, the estimation and prediction of the failure rate is not well. In this study, we study the quality of the maximum likelihood, asymptotic maximum likelihood and bias-corrected maximum likelihood methods, and propose a smooth adaptive estimation method for estimating the Log-logistic distribution parameters. To reduce the bias of the asymptotic maximum likelihood and smooth adaptive estimators of the Log-logistic distribution parameters, the bias-corrected method is used to improve the asymptotic maximum likelihood and smooth adaptive estimation methods. Two new bias-corrected estimation methods are also proposed to obtain reliable estimates of the Log-logistic distribution parameters. An intensive Monte Carlo simulation study is conducted to evaluate the performance of these estimation methods. Simulation results show that the smooth adaptive and two new bias-corrected estimation methods are more competitive than other competitors. Finally, two real example is used for illustrating the applications of the smooth adaptive, CAML and CSA estimation methods.