Uniqueness of maximum likelihood estimators of the 2-parameter Weibull distribution

1997 ◽  
Vol 46 (4) ◽  
pp. 523-525 ◽  
Author(s):  
N.R. Farnum ◽  
P. Booth
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Maximum Likelihood Estimators

Estimation of Weibull Parameters Using Fractional Moments

1984 ◽  
Vol 33 (3-4) ◽  
pp. 179-186 ◽  
Author(s):  
S.P. Mukherjee ◽  
B.C. Sasmal
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Relative Efficiency ◽  
Maximum Likelihood Estimators ◽  
Weibull Parameters ◽  
Moment Estimators ◽  
Fractional Moments ◽  
Distribution Moment ◽  
The Moment ◽  
Two Parameter

For a two-parameter Weibull distribution, moment estimators of the parameters have been developed by choosing orders of two moments (allowing fractions) so that the overall relative efficiency of the moment estimators compared with the maximum likelihood estimators is maximized . Some calculations in support of the superiority of fractional moments over integer moments in this connection have also been presented.

Finding maximum likelihood estimators for the three-parameter Weibull distribution

1994 ◽  
Vol 5 (4) ◽  
pp. 373-397 ◽  
Author(s):  
�ric Gourdin ◽  
Pierre Hansen ◽  
Brigitte Jaumard
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Maximum Likelihood Estimators

scholarly journals A New Flexible Family of Continuous Distributions: The Additive Odd-G Family

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1837
Author(s):  
Emrah Altun ◽  
Mustafa Ç. Korkmaz ◽  
Mahmoud El-Morshedy ◽  
Mohamed S. Eliwa
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Additive Model ◽  
Maximum Likelihood Estimators ◽  
Model Parameters ◽  
Model Structure ◽  
Simulation Studies ◽  
New Family ◽  
Family Based ◽  
Family Of Distributions

This paper introduces a new family of distributions based on the additive model structure. Three submodels of the proposed family are studied in detail. Two simulation studies were performed to discuss the maximum likelihood estimators of the model parameters. The log location-scale regression model based on a new generalization of the Weibull distribution is introduced. Three datasets were used to show the importance of the proposed family. Based on the empirical results, we concluded that the proposed family is quite competitive compared to other models.

scholarly journals A New Exponential-X Family: Modeling Extreme Value Data in the Finance Sector

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zubair Ahmad ◽  
Eisa Mahmoudi ◽  
Rasool Roozegar ◽  
Morad Alizadeh ◽  
Ahmed Z. Afify
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Statistical Models ◽  
Claims Data ◽  
Maximum Likelihood Estimators ◽  
Weibull Model ◽  
Insurance Claims Data ◽  
Practical Analysis ◽  
Heavy Tailed ◽  
Modeling Data

In this paper, a family of statistical models, namely, a new exponential-X family is proposed. A subcase of the introduced family, called the new exponential-Weibull (NE-Weibull) model, is studied. The NE-Weibull model is very competent and possesses heavy-tailed properties. The maximum likelihood estimators of its parameters are derived. The consistency and efficiency of these estimators are assessed in a brief simulation study. Finally, the effectiveness of the NE-Weibull distribution is illustrated by modeling real insurance claims data. The practical analysis shows that the NE-Weibull distribution outclassed other distributions and it can be a better choice for modeling data in the finance sector.

scholarly journals Reliability estimation and parameter estimation for inverse Weibull distribution under different loss functions

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Asuman Yilmaz ◽  
◽  
Mahmut Kara ◽  
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Maximum Likelihood Estimators ◽  
Loss Functions ◽  
Estimation Methods ◽  
Mcmc Methods ◽  
Inverse Weibull Distribution ◽  
Mean Square Errors ◽  
Bayesian Estimators

In this paper, the classical and Bayesian estimators of the unknown parameters and the reliability function of the inverse Weibull distribution are considered. The maximum likelihood estimators (MLEs) and modified maximum likelihood estimators (MMLEs) are used in the classical parameter estimation. Bayesian estimators of the parameters are obtained by using symmetric and asymmetric loss functions under informative and non-informative priors. Bayesian computations are derived by using Lindley approximation and Markov chain Monte Carlo (MCMC) methods. The asymptotic confidence intervals are constructed based on the maximum likelihood estimators. The Bayesian credible intervals of the parameters are obtained by using the MCMC method. Furthermore, the performances of these estimation methods are compared concerning their biases and mean square errors through a simulation study. It is seen that the Bayes estimators perform better than the classical estimators. Finally, two real-life examples are given for illustrative purposes.

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