scholarly journals Inference for the Geometric Extreme Exponential Distribution under Progressive Type II Censoring

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Reza Pakyari
Keyword(s):  
Confidence Intervals ◽  
Skewed Distribution ◽  
Type Ii ◽  
Monte Carlo Simulation Study ◽  
Censored Samples ◽  
Percentage Points ◽  
Progressive Type ◽  
Coverage Probabilities ◽  
Wide Range ◽  
Asymptotic Confidence Intervals

Geometric extreme exponential (GE-exponential) is one of the nonnegative right-skewed distribution that is suitable for analyzing lifetime data. It is well known that the maximum likelihood estimators (MLEs) of the parameters lead to likelihood equations that have to be solved numerically. In this paper, we provide explicit estimators through an approximation of the likelihood equations based on progressively Type-II-censored samples. The approximate estimators are then used as starting values to find the MLEs numerically. The bias and variances of the MLEs are calculated for a wide range of sample sizes and different progressive censoring schemes through a Monte Carlo simulation study. Moreover, formulas for the observed Fisher information are given which could be used to construct asymptotic confidence intervals. The coverage probabilities of the confidence intervals and the percentage points of pivotal quantities associated with the MLEs are also calculated. A real dataset has been studied for illustrative purposes.

scholarly journals Estimating the Parameters of the Exponential-Geometric distribution based on progressively type-II censored data

2018 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Aisha Fayomi ◽  
Hamdah Al-Shammari
Keyword(s):  
Censored Data ◽  
Geometric Distribution ◽  
Asymptotic Variance ◽  
Parameters Estimation ◽  
Type Ii ◽  
Progressive Type ◽  
The Em Algorithm ◽  
Asymptotic Confidence Intervals ◽  
Distribution Parameters ◽  
Type Ii Censored Data

This paper deals with the problem of parameters estimation of the Exponential-Geometric (EG) distribution based on progressive type-II censored data. It turns out that the maximum likelihood estimators for the distribution parameters have no closed forms, therefore the EM algorithm are alternatively used. The asymptotic variance of the MLEs of the targeted parameters under progressive type-II censoring is computed along with the asymptotic confidence intervals. Finally, a simple numerical example is given to illustrate the obtained results.

Reliability Estimation for the Two-Parameter Exponential Family Based on Progressively Type-II Censored Data

2018 ◽  
Vol 25 (01) ◽  
pp. 1850005
Author(s):  
Wenhao Gui
Keyword(s):  
Reliability Function ◽  
Reliability Estimation ◽  
Type Ii ◽  
Prior Density ◽  
Monte Carlo Simulation Study ◽  
Censored Samples ◽  
Type Ii Censored Data ◽  
Two Parameters ◽  
Parameter Case ◽  
Two Parameter

In this paper, we deal with the problem of estimating the reliability function of the two-parameter exponential distribution. Classical Maximum likelihood and Bayes estimates for one and two parameters and the reliability function are obtained on the basis of progressively type-II censored samples. The inverted gamma conjugate prior density is assumed for the one-parameter case, whereas the joint prior density of the two-parameter case is composed of the inverted gamma and the uniform densities. A comparison between the obtained estimators is made through a Monte Carlo simulation study. A real example is used to illustrate the proposed methods.

Reliability Estimation for Cold Standby Series System Based on General Progressive Type II Censored Samples

2013 ◽  
Vol 321-324 ◽  
pp. 2460-2463 ◽  
Author(s):  
Yi Min Shi ◽  
Xiao Lin Shi
Keyword(s):  
Bayesian Estimation ◽  
Reliability Function ◽  
Reliability Estimation ◽  
Likelihood Method ◽  
Series System ◽  
Type Ii ◽  
Censored Samples ◽  
Progressive Type ◽  
Cold Standby ◽  
Two Parameter

Suppose that the life of unit is distributed as two-parameter exponential distribution. The Bayesian estimation for cold standby series system is studied based on general Progressive type II censored samples. Under the different error loss, the Bayesian estimation of the unknown parameter and reliability function are derived where hyper-parameters are estimated by using Maximum likelihood method. At last, a numerical example is given by means of the Monte-Carlo simulation to illustrate the correctness and feasibility for the method proposed in this paper.

scholarly journals Progressively Type-II Right Censored Order Statistics from Hjorth Distribution and Related Inference

2020 ◽  
Vol 8 (2) ◽  
pp. 481-498
Author(s):  
NARINDER PUSHKARNA ◽  
JAGDISH SARAN ◽  
KANIKA VERMA
Keyword(s):  
Order Statistics ◽  
Recurrence Relations ◽  
Type Ii ◽  
Sample Sizes ◽  
Product Moments ◽  
Scale Parameters ◽  
Censored Samples ◽  
Progressive Type ◽  
Right Censored Order Statistics ◽  
Single And Product Moments

In this paper some recurrence relations satisfied by single and product moments of progressive Type-II right censored order statistics from Hjorth distribution have been obtained. Then we use these results to compute the moments for all sample sizes and all censoring schemes (R1,R2,...,Rm),m ≤ n, which allow us to obtain BLUEs of location and scale parameters based on progressive type-II right censored samples.

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