Estimation of parameters of inverse Weibull distribution and application to multi-component stress-strength model

2020 ◽  
pp. 1-26
Author(s):  
Nabakumar Jana ◽  
Samadrita Bera
Keyword(s):  
Weibull Distribution ◽  
Strength Model ◽  
Estimation Of Parameters ◽  
Inverse Weibull Distribution

scholarly journals Transmuted Modified Inverse Weibull distribution: Properties and application

2019 ◽  
pp. 667-677
Author(s):  
Muhammad Shuaib Khan
Keyword(s):  
Weibull Distribution ◽  
Geometric Mean ◽  
Life Time ◽  
Harmonic Mean ◽  
Potential Usefulness ◽  
Parameter Distribution ◽  
Estimation Of Parameters ◽  
Real Dataset ◽  
Special Cases ◽  
Inverse Weibull Distribution

This paper examines the potential usefulness of the transmuted modified inverse Weibull distribution. This four-parameter distribution holds eleven life time distributions as special cases. Theoretical properties of the transmuted modified inverse Weibull distribution are studied; which includes the quantile, median, entropy, mean deviations, mean, geometric mean and harmonic mean. The estimation of parameters is obtained by using the method of maximumlikelihood. An application to real dataset is provided to show the better fit of the transmuted modified inverse Weibull distribution.

A Note On Beta Inverse-Weibull Distribution

2013 ◽  
Vol 42 (2) ◽  
pp. 320-335 ◽  
Author(s):  
Sharoon Hanook ◽  
Muhammad Qaiser Shahbaz ◽  
Muhammad Mohsin ◽  
B. M. Golam Kibria
Keyword(s):  
Weibull Distribution ◽  
Inverse Weibull Distribution

scholarly journals Statistical Inference for the Weibull Distribution Based on δ-Record Data

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Raúl Gouet ◽  
F. Javier López ◽  
Lina Maldonado ◽  
Gerardo Sanz
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Statistical Inference ◽  
Numerical Computation ◽  
Weibull Model ◽  
Rainfall Series ◽  
Estimation Of Parameters ◽  
Record Data

We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from δ -record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on δ -record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design.

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