Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model
In this paper, we use the generalized progressive hybrid censoring sample from the Burr Type-Ⅻ distribution to estimate the unknown parameters, reliability and hazard functions. We apply the maximum likelihood (ML) and the Bayesian estimation under different prior distributions and different loss fu...
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doaj-5d2d62e5512f446c87a0c06394cd187b2021-07-08T02:34:32ZengAIMS PressAIMS Mathematics2473-69882021-05-01699675970410.3934/math.2021564Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime modelMagdy Nagy0Khalaf S. Sultan1ahmoud H. Abu-Moussa21. Department of Statistics and Operation Research, Faculty of Science, King Saud University, KSA 2. Department of Mathematics, Faculty of Science, Fayoum University, Fayoum, Egypt1. Department of Statistics and Operation Research, Faculty of Science, King Saud University, KSA3. Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt4. Department of Mathematics, Faculty of Science, Cairo University, Giza, EgyptIn this paper, we use the generalized progressive hybrid censoring sample from the Burr Type-Ⅻ distribution to estimate the unknown parameters, reliability and hazard functions. We apply the maximum likelihood (ML) and the Bayesian estimation under different prior distributions and different loss functions; namely; are the squared error, Linex and general entropy. Also, we construct the classical and credible intervals of the unknown parameters as well as for the survival and hazard functions. In addition, we investigate the performance of the point estimation by using the mean square error (MSE) and expected bias (EB) and performance of the interval estimation using the average length and coverage probability. Further, we develop the Bayesian one- and two- samples Bayesan prediction for the non-observed failures in the progressive censoring. In order to show the performance and usefulness of the inferential procedures, we carry out some simulation experiments using MCMC Algorithm for the Bayesian approach based on different prior distributions. Finally, we apply the theatrical finding to some real life data set.https://www.aimspress.com/article/doi/10.3934/math.2021564?viewType=HTMLbayesian estimationbayesian predictionburr type-ⅻ distributionmaximum likelihood estimationgeneralized progressive hybrid censoring samplelinexasymmetric and general entropy loss function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Magdy Nagy Khalaf S. Sultan ahmoud H. Abu-Moussa |
spellingShingle |
Magdy Nagy Khalaf S. Sultan ahmoud H. Abu-Moussa Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model AIMS Mathematics bayesian estimation bayesian prediction burr type-ⅻ distribution maximum likelihood estimation generalized progressive hybrid censoring sample linex asymmetric and general entropy loss function |
author_facet |
Magdy Nagy Khalaf S. Sultan ahmoud H. Abu-Moussa |
author_sort |
Magdy Nagy |
title |
Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model |
title_short |
Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model |
title_full |
Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model |
title_fullStr |
Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model |
title_full_unstemmed |
Analysis of the generalized progressive hybrid censoring from Burr Type-Ⅻ lifetime model |
title_sort |
analysis of the generalized progressive hybrid censoring from burr type-ⅻ lifetime model |
publisher |
AIMS Press |
series |
AIMS Mathematics |
issn |
2473-6988 |
publishDate |
2021-05-01 |
description |
In this paper, we use the generalized progressive hybrid censoring sample from the Burr Type-Ⅻ distribution to estimate the unknown parameters, reliability and hazard functions. We apply the maximum likelihood (ML) and the Bayesian estimation under different prior distributions and different loss functions; namely; are the squared error, Linex and general entropy. Also, we construct the classical and credible intervals of the unknown parameters as well as for the survival and hazard functions. In addition, we investigate the performance of the point estimation by using the mean square error (MSE) and expected bias (EB) and performance of the interval estimation using the average length and coverage probability. Further, we develop the Bayesian one- and two- samples Bayesan prediction for the non-observed failures in the progressive censoring. In order to show the performance and usefulness of the inferential procedures, we carry out some simulation experiments using MCMC Algorithm for the Bayesian approach based on different prior distributions. Finally, we apply the theatrical finding to some real life data set. |
topic |
bayesian estimation bayesian prediction burr type-ⅻ distribution maximum likelihood estimation generalized progressive hybrid censoring sample linex asymmetric and general entropy loss function |
url |
https://www.aimspress.com/article/doi/10.3934/math.2021564?viewType=HTML |
work_keys_str_mv |
AT magdynagy analysisofthegeneralizedprogressivehybridcensoringfromburrtypexiilifetimemodel AT khalafssultan analysisofthegeneralizedprogressivehybridcensoringfromburrtypexiilifetimemodel AT ahmoudhabumoussa analysisofthegeneralizedprogressivehybridcensoringfromburrtypexiilifetimemodel |
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1721314542009450496 |