Bayesian estimation of parameters of inverse Weibull distribution

2013 ◽  
Vol 40 (7) ◽  
pp. 1597-1607 ◽  
Author(s):  
Sanjay Kumar Singh ◽  
Umesh Singh ◽  
Dinesh Kumar
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Estimation Of Parameters ◽  
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.

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.

BAYESIAN ESTIMATION IN RANDOM CENSORSHIP MODEL FOR WEIBULL DISTRIBUTION UNDER DIFFERENT LOSS FUNCTIONS

2012 ◽  
Vol 04 (03) ◽  
pp. 1250021 ◽  
Author(s):  
MUHAMMAD YAMEEN DANISH ◽  
MUHAMMAD ASLAM
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Real Data ◽  
Loss Functions ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Random Censorship

This paper deals with Bayesian estimation of parameters in the proportional hazards model of random censorship for the Weibull distribution under different loss functions. We consider both the informative and noninformative priors on the model parameters to obtain the Bayes estimates using Gibbs sampling scheme. Maximum likelihood estimates are also obtained for comparison purposes. A simulation study is carried out to observe the behavior of the proposed estimators for different sample sizes and for different censoring parameters. One real data analysis is performed for illustration.

scholarly journals A new inverse Weibull distribution: properties, classical and Bayesian estimation with applications

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Ahmed Z. Afify ◽  
◽  
Ahmed I. Shawky ◽  
Mazen Nassar ◽  
◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Exponential Distributions ◽  
Unknown Parameters ◽  
Large Samples ◽  
Mathematical Properties ◽  
Inverse Weibull Distribution

This article proposes a new extension of the inverse Weibull distribution called, logarithmic transformed inverse Weibull distribution which can provide better fits than some of its well-known extensions. The proposed distribution contains inverse Weibull, inverse Rayleigh, inverse exponential, logarithmic transformed inverse Rayleigh and logarithmic transformed inverse exponential distributions as special sub-models. Our main focus is to derive some of its mathematical properties along with the estimation of its unknown parameters using frequentist and Bayesian estimation methods. We compare the performances of the proposed estimators using extensive numerical simulations for both small and large samples. The importance and potentiality of this distribution is analyzed via two real data sets.

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