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.

A SIMPLE ESTIMATOR FOR THE WEIBULL SHAPE PARAMETER

2012 ◽  
Vol 12 (02) ◽  
pp. 395-402 ◽  
Author(s):  
MAHDI TEIMOURI ◽  
SARALEES NADARAJAH
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Closed Form ◽  
Maximum Likelihood Estimator ◽  
Shape Parameter ◽  
Scale Parameter ◽  
Maximum Likelihood Estimators ◽  
Simulation Studies ◽  
Likelihood Estimator

The Weibull distribution is the most popular model for lifetimes. However, the maximum likelihood estimators for the Weibull distribution are not available in closed form. In this note, we derive a simple, consistent, closed form estimator for the Weibull shape parameter. This estimator is independent of the Weibull scale parameter. Simulation studies show that this estimator performs as well as the maximum likelihood estimator.

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