Abstract
Several studies have considered various scheduling methods and reliability functions to determine the optimum maintenance time. These methods and functions correspond to the lowest cost by using the maximum likelihood estimator to evaluate the model parameters. However, this paper aims to estimate the parameters of the two-parameter Weibull distribution (α, β). The maximum likelihood estimation method, modified linear exponential loss function, and Wyatt-based regression method are used for the estimation of the parameters. Minimum mean square error (MSE) criterion is used to evaluate the relative efficiency of the estimators. The comparison of the different parameter estimation methods is conducted, and the efficiency of these methods is observed, both mathematically and experimentally. The simulation study is conducted for comparison of samples sizes (10, 50, 100, 150) based on the mean square error (MSE). It is concluded that the maximum likelihood method was found to be the most efficient method for all sample sizes used in the research because it achieved the least MSE compared with other methods.
Highlights
Introduction e Weibull distribution is the continuous probability distribution. It has been frequently used as a failure model in reliability studies to describe all phases of machine failure [1, 2]
For the relevant information and details on applications, we recommend the readers to read several studies proposed in [4, 6, 8, 11,12,13,14]. e Bayesian estimation is commonly used in various scientific areas [15,16,17]. ere exists much literature on the subject; due to the limitations of the paper, we are incapable of relating all related information
We describe different parameter estimation methods for Weibull distribution
Summary
Introduction eWeibull distribution is the continuous probability distribution. It has been frequently used as a failure model in reliability studies to describe all phases of machine failure [1, 2]. We describe different parameter estimation methods for Weibull distribution.
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