Bias Corrected Weibull Parameter Estimation and Impact on Confidence Bounds

2020 ◽  
Author(s):  
Tamer Tevetoglu ◽  
Bernd Bertsche
Keyword(s):  
Parameter Estimation ◽  
Weibull Parameter ◽  
Confidence Bounds

Using real-coded genetic algorithms for Weibull parameter estimation

1995 ◽  
Vol 29 (1-4) ◽  
pp. 377-381 ◽  
Author(s):  
G.M. Thomas ◽  
R. Gerth ◽  
T. Velasco ◽  
L.C. Rabelo
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Weibull Parameter ◽  
Real Coded Genetic Algorithms

scholarly journals Age replacement policy in the case of no data: the effect of Weibull parameter estimation

2019 ◽  
Vol 58 (19) ◽  
pp. 5851-5869 ◽  
Author(s):  
Fabio Sgarbossa ◽  
Ilenia Zennaro ◽  
Eleonora Florian ◽  
Martina Calzavara
Keyword(s):  
Parameter Estimation ◽  
Replacement Policy ◽  
Weibull Parameter ◽  
Age Replacement

Weibull parameter estimation

1982 ◽  
Vol 22 (4) ◽  
pp. 681-684 ◽  
Author(s):  
Karen Fung ◽  
A.K.S. Jardine
Keyword(s):  
Parameter Estimation ◽  
Weibull Parameter

scholarly journals The Exponentiated Exponential Burr XII distribution: Theory and application to lifetime and simulated data

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248873
Author(s):  
Majdah Badr ◽  
Muhammad Ijaz
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Probability Distribution ◽  
Simulation Study ◽  
Probability Distributions ◽  
Simulated Data ◽  
Maximum Likelihood Estimators ◽  
Data Sets ◽  
Lifetime Data ◽  
Confidence Bounds

The paper addresses a new four-parameter probability distribution called the Exponentiated Exponential Burr XII or abbreviated as EE-BXII. We derive various statistical properties in addition to the parameter estimation, moments, and asymptotic confidence bounds. We estimate the precision of the maximum likelihood estimators via a simulation study. Furthermore, the utility of the proposed distribution is evaluated by using two lifetime data sets and the results are compared with other existing probability distributions. The results clarify that the proposed distribution provides a better fit to these data sets as compared to the existing probability distributions.

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