Selection of Efficient Parameter Estimation Method for Two-Parameter Weibull Distribution

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 t...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2021-11, Vol.2021, p.1-8
Main Authors: Almazah, Mohammed M. A., Ismail, Muhammad
Format: Article
Language:English
Subjects:
Efficiency
Estimates
Maximum likelihood estimation
Maximum likelihood estimators
Methods
Parameter estimation
Parameter modification
Statistical analysis
Weibull distribution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5806068