On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data

2008 ◽  
Vol 78 (17) ◽  
pp. 2971-2975 ◽  
Author(s):  
N. Balakrishnan ◽  
M. Kateri
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Censored Data ◽  
Likelihood Estimation ◽  
Estimation Of Parameters

Random Censored Data MLE of Weibull Distribution Based on Simulink

2012 ◽  
Vol 542-543 ◽  
pp. 1463-1466
Author(s):  
Jing Zhao Shi ◽  
Rui Na Shao ◽  
Xiang Fu Li
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Censored Data ◽  
Computer Programming ◽  
Likelihood Estimation ◽  
Random Censoring ◽  
Life Data ◽  
Transcendental Equations

Maximum likelihood estimation (MLE) is a commonly used method of Weibull distribution, but it needs to calculate the transcendental equations which is based on the computer programming. In this paper, we established a new MLE model of Weibull distribution in a case of random censoring. And this model was proved feasible and correct via three examples. At the same time, the model can be used for a variety of censored data and complete life data of Weibull distribution of MLE.

scholarly journals Maximum Likelihood Estimation of Parameters for Advanced Continuously Reinforced Concrete Pavement (CRCP) Punchout Calibration Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liangliang Chen ◽  
Feng Zhang ◽  
Changjun Zhou
Keyword(s):  
Reinforced Concrete ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Likelihood Estimation ◽  
Concrete Pavement ◽  
Pavement Design ◽  
Regression Result ◽  
Calibration Model ◽  
Estimation Of Parameters

Pavement performance prediction is the essential part of the pavement design, which is very important for highway agencies for the purpose of budget allocating. This study introduces a model of local calibration for punchout, which is the major structural distress of continuously reinforced concrete pavement (CRCP). It is assumed that the number of equivalent single axle loads’ (ESALs) leads to punchout follows a Weibull distribution. The parameters of Weibull distribution were estimated by maximum likelihood estimation (MLE). Additionally, an approach of estimating the initial value of the parameters was also presented before applying the Newton method for solving the likelihood equations. The regression result was found to fit the performance-monitoring data from LTPP very well. The proposed calibration model is capable of describing the punchout and can be employed to predict the failure rate and reliability of CRCP in the pavement design and the arrangement of rehabilitation activities.

Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution

2022 ◽  
Vol 7 (2) ◽  
pp. 2820-2839
Author(s):  
Saurabh L. Raikar ◽  
◽  
Dr. Rajesh S. Prabhu Gaonkar ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Jaya Algorithm ◽  
Two Parameter

<abstract> <p>Jaya algorithm is a highly effective recent metaheuristic technique. This article presents a simple, precise, and faster method to estimate stress strength reliability for a two-parameter, Weibull distribution with common scale parameters but different shape parameters. The three most widely used estimation methods, namely the maximum likelihood estimation, least squares, and weighted least squares have been used, and their comparative analysis in estimating reliability has been presented. The simulation studies are carried out with different parameters and sample sizes to validate the proposed methodology. The technique is also applied to real-life data to demonstrate its implementation. The results show that the proposed methodology's reliability estimates are close to the actual values and proceeds closer as the sample size increases for all estimation methods. Jaya algorithm with maximum likelihood estimation outperforms the other methods regarding the bias and mean squared error.</p> </abstract>

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