scholarly journals Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hanan Haj Ahmad
Keyword(s):  
Interval Estimation ◽  
Prediction Method ◽  
Estimation Methods ◽  
Type Ii ◽  
Unknown Parameters ◽  
Monte Carlo Techniques ◽  
Censored Samples ◽  
Pareto Model ◽  
Progressive Type ◽  
Mean Square Errors

This paper describes two prediction methods for predicting the non-observed (censored) units under progressive Type-II censored samples. The lifetimes under consideration are following a new two-parameter Pareto distribution. Furthermore, point and interval estimation of the unknown parameters of the new Pareto model is obtained. Maximum likelihood and Bayesian estimation methods are considered for that purpose. Since Bayes estimators cannot be expressed explicitly, Gibbs and the Markov Chain Monte Carlo techniques are utilized for Bayesian calculation. We use the posterior predictive density of the non-observed units to construct predictive intervals. A simulation study is performed to evaluate the performance of the estimators via mean square errors and biases and to obtain the best prediction method for the censored observation under progressive Type-II censoring scheme for different sample sizes and different censoring schemes.

scholarly journals On Progressive Type-II Censored Samples from Alpha Power Exponential Distribution

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Mukhtar M. Salah
Keyword(s):  
Exponential Distribution ◽  
Interval Estimation ◽  
Location Parameter ◽  
Alpha Power ◽  
Type Ii ◽  
Unknown Parameters ◽  
Progressive Type ◽  
Type Ii Censored Data ◽  
Best Linear Unbiased Estimators ◽  
Two Parameter

In this paper the two-parameter α -power exponential distribution is studied. We study the two-parameter α -power exponential μ , λ distribution with the location parameter μ > 0 and scale parameter λ > 0 under progressive Type-II censored data with fixed shape parameter α . We estimate the maximum likelihood estimators of these unknown parameters numerically since it cannot be solved analytically. We use the approximate best linear unbiased estimators μ ∗ and λ ∗ , as an initial guesses to obtain the MLEs μ ^ and λ ^ . We estimate the interval estimation of these unknowns’ parameters. Monte Carlo simulations are performed and data examples have been provided for illustration and comparison.

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 Bayesian Estimation for Inverse Weibull Distribution Under Progressive Type-II Censored Data With Beta-Binomial Removals

2018 ◽  
Vol 47 (1) ◽  
pp. 77-94
Author(s):  
Pradeep Kumar Vishwakarma ◽  
Arun Kaushik ◽  
Aakriti Pandey ◽  
Umesh Singh ◽  
Sanjay Kumar Singh
Keyword(s):  
Weibull Distribution ◽  
Real Data ◽  
Estimation Procedure ◽  
Type Ii ◽  
Unknown Parameters ◽  
Data Set ◽  
Progressive Type ◽  
Inverse Weibull Distribution ◽  
Type Ii Censored Data ◽  
Censoring Scheme

This paper deals with the estimation procedure for inverse Weibull distribution under progressive type-II censored samples when removals follow Beta-binomial probability law. To estimate the unknown parameters, the maximum likelihood and Bayes estimators are obtained under progressive censoring scheme mentioned above. Bayes estimates are obtained using Markov chain Monte Carlo (MCMC) technique considering square error loss function and compared with the corresponding MLE's. Further, the expected total time on test is obtained under considered censoring scheme.  Finally, a real data set has been analysed to check the validity of the study.

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.

A Simple Simulational Algorithm for Generating Progressive Type-II Censored Samples

1995 ◽  
Vol 49 (2) ◽  
pp. 229-230 ◽  
Author(s):  
N. Balakrishnan ◽  
R. A. Sandhu
Keyword(s):  
Type Ii ◽  
Censored Samples ◽  
Progressive Type
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