The present article aims to illustrate how the Adaptive Type-II Progressive Hybrid censoring scheme can be used to make statistical inferences regarding the shape parameters of the Kumaraswamy distribution. By adopting this scheme, one can reduce the total testing time and the cost associated with the failure of the units. Best of all, one can increase the effectiveness of the statistical analysis while reducing the total test time. The maximum product of spacings method (MPS) in classical estimation settings is highly effective. According to several authors, this method is a superior alternative to the maximum likelihood estimation method (MLE), which delivers more accurate estimates than the maximum likelihood estimation method. Our goal in this article is to estimate the shape parameters of the Kumaraswamy distribution by utilizing the MPS method. Asymptotic normality properties of the estimators are implemented to obtain approximate confidence intervals. In addition, bootstrap confidence intervals are calculated. Monte Carlo simulations have been carried out to compare the MPS and MLE methods. In order to assess the effectiveness of the proposed procedure, a numerical example based on real data is presented.
| Published in | Applied and Computational Mathematics (Volume 11, Issue 4) |
| DOI | 10.11648/j.acm.20221104.13 |
| Page(s) | 102-115 |
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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APA Style
Amal Helu. (2022). Adaptive Type-II Hybrid Progressive Schemes Based on Maximum Product of Spacings for Parameter Estimation of Kumaraswamy Distribution. Applied and Computational Mathematics, 11(4), 102-115. https://doi.org/10.11648/j.acm.20221104.13
ACS Style
Amal Helu. Adaptive Type-II Hybrid Progressive Schemes Based on Maximum Product of Spacings for Parameter Estimation of Kumaraswamy Distribution. Appl. Comput. Math. 2022, 11(4), 102-115. doi: 10.11648/j.acm.20221104.13
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Download@article{10.11648/j.acm.20221104.13,
author = {Amal Helu},
title = {Adaptive Type-II Hybrid Progressive Schemes Based on Maximum Product of Spacings for Parameter Estimation of Kumaraswamy Distribution},
journal = {Applied and Computational Mathematics},
volume = {11},
number = {4},
pages = {102-115},
doi = {10.11648/j.acm.20221104.13},
url = {https://doi.org/10.11648/j.acm.20221104.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20221104.13},
abstract = {The present article aims to illustrate how the Adaptive Type-II Progressive Hybrid censoring scheme can be used to make statistical inferences regarding the shape parameters of the Kumaraswamy distribution. By adopting this scheme, one can reduce the total testing time and the cost associated with the failure of the units. Best of all, one can increase the effectiveness of the statistical analysis while reducing the total test time. The maximum product of spacings method (MPS) in classical estimation settings is highly effective. According to several authors, this method is a superior alternative to the maximum likelihood estimation method (MLE), which delivers more accurate estimates than the maximum likelihood estimation method. Our goal in this article is to estimate the shape parameters of the Kumaraswamy distribution by utilizing the MPS method. Asymptotic normality properties of the estimators are implemented to obtain approximate confidence intervals. In addition, bootstrap confidence intervals are calculated. Monte Carlo simulations have been carried out to compare the MPS and MLE methods. In order to assess the effectiveness of the proposed procedure, a numerical example based on real data is presented.},
year = {2022}
}
TY - JOUR T1 - Adaptive Type-II Hybrid Progressive Schemes Based on Maximum Product of Spacings for Parameter Estimation of Kumaraswamy Distribution AU - Amal Helu Y1 - 2022/08/17 PY - 2022 N1 - https://doi.org/10.11648/j.acm.20221104.13 DO - 10.11648/j.acm.20221104.13 T2 - Applied and Computational Mathematics JF - Applied and Computational Mathematics JO - Applied and Computational Mathematics SP - 102 EP - 115 PB - Science Publishing Group SN - 2328-5613 UR - https://doi.org/10.11648/j.acm.20221104.13 AB - The present article aims to illustrate how the Adaptive Type-II Progressive Hybrid censoring scheme can be used to make statistical inferences regarding the shape parameters of the Kumaraswamy distribution. By adopting this scheme, one can reduce the total testing time and the cost associated with the failure of the units. Best of all, one can increase the effectiveness of the statistical analysis while reducing the total test time. The maximum product of spacings method (MPS) in classical estimation settings is highly effective. According to several authors, this method is a superior alternative to the maximum likelihood estimation method (MLE), which delivers more accurate estimates than the maximum likelihood estimation method. Our goal in this article is to estimate the shape parameters of the Kumaraswamy distribution by utilizing the MPS method. Asymptotic normality properties of the estimators are implemented to obtain approximate confidence intervals. In addition, bootstrap confidence intervals are calculated. Monte Carlo simulations have been carried out to compare the MPS and MLE methods. In order to assess the effectiveness of the proposed procedure, a numerical example based on real data is presented. VL - 11 IS - 4 ER -
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