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Year 2022, Volume: 35 Issue: 1, 293 - 312, 01.03.2022
https://doi.org/10.35378/gujs.741755

Abstract

References

  • [1] Marshall, A. W., & Olkin, I., “A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families”, Biometrika, 84(3): 641-652, (1997). ‏
  • [2] Ghitany, M. E., Al-Hussaini, E. K., & Al-Jarallah, R. A., “Marshall–Olkin extended Weibull distribution and its application to censored data”, Journal of Applied Statistics, 32(10): 1025-1034, (2005).
  • [3] Ghitany, M. E., Al-Awadhi, F. A., & Alkhalfan, L. A., “Marshall–Olkin extended Lomax distribution and its application to censored data”, Communications in Statistics-Theory and Methods, 36(10): 1855-1866, (2007).
  • [4] Alice, T., & Jose, K. K., “Marshall-Olkin logistic processes”, STARS Int Journal, 6(1): 1-11, (2005).
  • [5] Okasha, H. M., & Kayid, M., “A new family of Marshall–Olkin extended generalized linear exponential distribution”, Journal of Computational and Applied Mathematics, 296: 576-592, (2016). ‏
  • [6]
  • Ahmad, H. H., & Almetwally, E., “Marshall-Olkin Generalized Pareto Distribution: Bayesian and Non-Bayesian Estimation”, Pakistan Journal of Statistics and Operation Research, 16(1): 21-33, (2020).‏
  • [7] Mahdavi, A., & Kundu, D., “A new method for generating distributions with an application to exponential distribution”, Communications in Statistics-Theory and Methods, 46(13): 6543-6557, (2017).
  • [8] Nassar, M., Alzaatreh, A., Mead, M., & Abo-Kasem, O., “Alpha power Weibull distribution: Properties and applications”, Communications in Statistics-Theory and Methods, 46(20): 10236-10252, (2017). ‏
  • [9] Elbatal, I., Ahmad, Z., Elgarhy, B. M., & Almarashi, A. M., “A New Alpha Power Transformed Family of Distributions: Properties and Applications to the Weibull Model”, Journal of Nonlinear Science and Applications, 12(1): 1-20, (2018).‏
  • [10] Dey, S., Ghosh, I., & Kumar, D., “Alpha-power transformed Lindley distribution: properties and associated inference with application to earthquake data”, Annals of Data Science, 6(4): 623-650, (2018).
  • [11] Dey, S., Nassar, M., & Kumar, D., “Alpha power transformed inverse Lindley distribution: A distribution with an upside-down bathtub-shaped hazard function”, Journal of Computational and Applied Mathematics, 348:130-145, (2019).‏ ‏
  • [12] Hassan, A. S., Elgarhy, M., Mohamd, R. E., & Alrajhi, S., (2019). “On the Alpha Power Transformed Power Lindley Distribution”, Journal of Probability and Statistics, 2019.‏
  • [13] Basheer, A. M., “Alpha power inverse Weibull distribution with reliability application”, Journal of Taibah University for Science, 13(1): 423-432, (2019).‏
  • [14] Nassar, M., Kumar, D., Dey, S., Cordeiro, G. M., & Afify, A. Z., “The Marshall–Olkin alpha power family of distributions with applications”, Journal of Computational and Applied Mathematics, 351: 41-53, (2019).
  • [15] Almetwally, E. M., & Ahmad, H. A. H., “A new generalization of the Pareto distribution and its applications”, Statistics in Transition New Series, 21(5): 61-84, (2020).
  • [16] Basheer, A. M., “Marshall–Olkin alpha power inverse exponential distribution: properties and applications”, Annals of data science, 1-13, (2019).
  • [17] Almetwally, E. M., Sabry, M. A., Alharbi, R., Alnagar, D., Mubarak, S. A., & Hafez, E. H., “Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation Based on Type-I and Type-II Censoring”, Complexity, (2021). https://doi.org/10.1155/2021/5533799
  • [18] Kyurkchiev, V., Iliev, A., Rahnev, A., & Kyurkchiev, N., “Some New Logistic Differential Models: Properties and Applications”, LAP LAMBERT Academic Publishing, (2019).
  • [19] Basheer, A. M., Almetwally, E. M., & Okasha, H. M., “Marshall-Olkin Alpha Power Inverse Weibull Distribution: Non Bayesian and Bayesian Estimations”, Journal of Statistics Applications & Probability, To apper, (2021).
  • [20] Yong, T., “Extended Weibull distributions in reliability engineering (Doctoral dissertation)”, ch. 4, (2004).
  • [21] Balakrishnan N., Ng H. K. T., “Precedence-type tests and applications”, Wiley, Hoboken, (2006).
  • [22] Balakrishnan, N., “Progressive censoring methodology: an appraisal”, Test, (2007).
  • [23] Almetwaly, E. M., & Almongy, H. M., “Estimation of the Generalized Power Weibull Distribution Parameters Using Progressive Censoring Schemes”, International Journal of Probability and Statistics, 7(2): 51-61, (2018). ‏
  • [24] Hassan, A. S., & Abd-Allah, M., “On the Inverse Power Lomax Distribution”, Annals of Data Science, 6(2): 259-278, (2019). ‏
  • [25] Almetwally, E. M., & Almongy, H. M., “Maximum Product Spacing and Bayesian Method for Parameter Estimation for Generalized Power Weibull Distribution under Censoring Scheme”, Journal of Data Science, 17(2): 407-444, (2019).
  • [26] Almetwally, E. M., & Almongy, H. M., “Estimation Methods for the New Weibull-Pareto Distribution: Simulation and Application”, Journal of Data Science, 17(3): 610-630, (2019).‏
  • [27] Kundu, D., & Howlader, H., “Bayesian inference and prediction of the inverse Weibull distribution for Type II censored data”, Computational Statistics & Data Analysis, 54(6): 1547-1558, (2010). ‏ ‏ [28] Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E., “Equation of state calculations by fast computing machines”, The journal of chemical physics, 21(6): 1087-1092, (1953).‏
  • [29] Nassar, M., Abo-Kasem, O., Zhang, C., & Dey, S., “Analysis of Weibull Distribution Under Adaptive Type II Progressive Hybrid Censoring Scheme”, Journal of the Indian Society for Probability and Statistics, 19(1): 25-65, (2018).
  • [30] Almetwally, E. M., Almongy, H. M., & El sayed Mubarak, A., “Bayesian and Maximum Likelihood Estimation for the Weibull Generalized Exponential Distribution Parameters Using Progressive Censoring Schemes”, Pakistan Journal of Statistics and Operation Research, 14(4): 853-868, (2018).‏
  • [31] Smith, R. L., & Naylor, J., “A comparison of maximum likelihood and Bayesian estimators for the three‐parameter Weibull distribution”, Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(3): 358-369, (1987).
  • [32] Tahir, M. H., Cordeiro, G. M., Mansoor, M., & Zubair, M. “The Weibull-Lomax distribution: properties and applications”, Hacettepe Journal of Mathematics and Statistics, 44(2): 461-480, (2015). ‏
  • [33] Badar, M. G., & Priest, A. M., “Statistical aspects of fiber and bundle strength in hybrids composite”, Progress in Science and Engineering Composites, 1129–1136, (1982).
  • [34] Dey, S., Dey, T., & Kundu, D., “Two-parameter Rayleigh distribution: different methods of estimation”, American Journal of Mathematical and Management Sciences, 33(1): 55-74, (2014).
  • [35] Ijaz, M., & Asim, S. M., “Lomax exponential distribution with an application to real-life data”, PloS one, 14(12): e0225827, (2019).
  • [36] Hassan, A., & Mohamed, R., “Parameter Estimation for Inverted Exponentiated Lomax Distribution with Right Censored Data”, Gazi University Journal of Science, 32(4): 1370-1386, (2019).
  • [37] Hassan, A., Elshrpieby, E., & Mohamed, R., “Odd Generalized Exponential Power Function Distribution: Properties & Applications”, Gazi University Journal of Science, 32(1): 351-370, (2019).
  • [38] Alshenawy, R., Al-Alwan, A., Almetwally, E. M., Afify, A. Z., & Almongy, H. M., “Progressive type II censoring schemes of extended odd Weibull exponential distribution with applications in medicine and engineering”, Mathematics, 8(10): 1679, (2020).
  • [39] Alshenawy, R., Sabry, M. A., Almetwally, E. M., & Almongy, H. M., “Product Spacing of Stress–Strength under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution”, Computers, Materials & Continua, 66(3): 2973-2995, (2021).
  • [40] Muhammed, H. Z., & Almetwally, E. M., “Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type II Censoring”, Annals of Data Science, 1-32, (2020).
  • [41] El-Morshedy, M., Alhussain, Z. A., Atta, D., Almetwally, E. M., & Eliwa, M. S., “Bivariate Burr X generator of distributions: properties and estimation methods with applications to complete and type II censored samples”, Mathematics, 8(2): 264, (2020).

Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring

Year 2022, Volume: 35 Issue: 1, 293 - 312, 01.03.2022
https://doi.org/10.35378/gujs.741755

Abstract

In this paper, we insert and study a novel five-parameter extended Weibull distribution denominated as the Marshall–Olkin alpha power extended Weibull (MOAPEW) distribution. This distribution's statistical properties are discussed. Maximum likelihood estimations (MLE), maximum product spacing (MPS), and Bayesian estimation for the MOAPEW distribution parameters are obtained using Type I and Type II censored samples. A numerical analysis using Monte-Carlo simulation and real data sets are realized to compare various estimation methods. The supremacy of this novel model upon some famous distributions is explicated using different real datasets as it appears the MOAPEW model achieves a good fit for these applications.

References

  • [1] Marshall, A. W., & Olkin, I., “A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families”, Biometrika, 84(3): 641-652, (1997). ‏
  • [2] Ghitany, M. E., Al-Hussaini, E. K., & Al-Jarallah, R. A., “Marshall–Olkin extended Weibull distribution and its application to censored data”, Journal of Applied Statistics, 32(10): 1025-1034, (2005).
  • [3] Ghitany, M. E., Al-Awadhi, F. A., & Alkhalfan, L. A., “Marshall–Olkin extended Lomax distribution and its application to censored data”, Communications in Statistics-Theory and Methods, 36(10): 1855-1866, (2007).
  • [4] Alice, T., & Jose, K. K., “Marshall-Olkin logistic processes”, STARS Int Journal, 6(1): 1-11, (2005).
  • [5] Okasha, H. M., & Kayid, M., “A new family of Marshall–Olkin extended generalized linear exponential distribution”, Journal of Computational and Applied Mathematics, 296: 576-592, (2016). ‏
  • [6]
  • Ahmad, H. H., & Almetwally, E., “Marshall-Olkin Generalized Pareto Distribution: Bayesian and Non-Bayesian Estimation”, Pakistan Journal of Statistics and Operation Research, 16(1): 21-33, (2020).‏
  • [7] Mahdavi, A., & Kundu, D., “A new method for generating distributions with an application to exponential distribution”, Communications in Statistics-Theory and Methods, 46(13): 6543-6557, (2017).
  • [8] Nassar, M., Alzaatreh, A., Mead, M., & Abo-Kasem, O., “Alpha power Weibull distribution: Properties and applications”, Communications in Statistics-Theory and Methods, 46(20): 10236-10252, (2017). ‏
  • [9] Elbatal, I., Ahmad, Z., Elgarhy, B. M., & Almarashi, A. M., “A New Alpha Power Transformed Family of Distributions: Properties and Applications to the Weibull Model”, Journal of Nonlinear Science and Applications, 12(1): 1-20, (2018).‏
  • [10] Dey, S., Ghosh, I., & Kumar, D., “Alpha-power transformed Lindley distribution: properties and associated inference with application to earthquake data”, Annals of Data Science, 6(4): 623-650, (2018).
  • [11] Dey, S., Nassar, M., & Kumar, D., “Alpha power transformed inverse Lindley distribution: A distribution with an upside-down bathtub-shaped hazard function”, Journal of Computational and Applied Mathematics, 348:130-145, (2019).‏ ‏
  • [12] Hassan, A. S., Elgarhy, M., Mohamd, R. E., & Alrajhi, S., (2019). “On the Alpha Power Transformed Power Lindley Distribution”, Journal of Probability and Statistics, 2019.‏
  • [13] Basheer, A. M., “Alpha power inverse Weibull distribution with reliability application”, Journal of Taibah University for Science, 13(1): 423-432, (2019).‏
  • [14] Nassar, M., Kumar, D., Dey, S., Cordeiro, G. M., & Afify, A. Z., “The Marshall–Olkin alpha power family of distributions with applications”, Journal of Computational and Applied Mathematics, 351: 41-53, (2019).
  • [15] Almetwally, E. M., & Ahmad, H. A. H., “A new generalization of the Pareto distribution and its applications”, Statistics in Transition New Series, 21(5): 61-84, (2020).
  • [16] Basheer, A. M., “Marshall–Olkin alpha power inverse exponential distribution: properties and applications”, Annals of data science, 1-13, (2019).
  • [17] Almetwally, E. M., Sabry, M. A., Alharbi, R., Alnagar, D., Mubarak, S. A., & Hafez, E. H., “Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation Based on Type-I and Type-II Censoring”, Complexity, (2021). https://doi.org/10.1155/2021/5533799
  • [18] Kyurkchiev, V., Iliev, A., Rahnev, A., & Kyurkchiev, N., “Some New Logistic Differential Models: Properties and Applications”, LAP LAMBERT Academic Publishing, (2019).
  • [19] Basheer, A. M., Almetwally, E. M., & Okasha, H. M., “Marshall-Olkin Alpha Power Inverse Weibull Distribution: Non Bayesian and Bayesian Estimations”, Journal of Statistics Applications & Probability, To apper, (2021).
  • [20] Yong, T., “Extended Weibull distributions in reliability engineering (Doctoral dissertation)”, ch. 4, (2004).
  • [21] Balakrishnan N., Ng H. K. T., “Precedence-type tests and applications”, Wiley, Hoboken, (2006).
  • [22] Balakrishnan, N., “Progressive censoring methodology: an appraisal”, Test, (2007).
  • [23] Almetwaly, E. M., & Almongy, H. M., “Estimation of the Generalized Power Weibull Distribution Parameters Using Progressive Censoring Schemes”, International Journal of Probability and Statistics, 7(2): 51-61, (2018). ‏
  • [24] Hassan, A. S., & Abd-Allah, M., “On the Inverse Power Lomax Distribution”, Annals of Data Science, 6(2): 259-278, (2019). ‏
  • [25] Almetwally, E. M., & Almongy, H. M., “Maximum Product Spacing and Bayesian Method for Parameter Estimation for Generalized Power Weibull Distribution under Censoring Scheme”, Journal of Data Science, 17(2): 407-444, (2019).
  • [26] Almetwally, E. M., & Almongy, H. M., “Estimation Methods for the New Weibull-Pareto Distribution: Simulation and Application”, Journal of Data Science, 17(3): 610-630, (2019).‏
  • [27] Kundu, D., & Howlader, H., “Bayesian inference and prediction of the inverse Weibull distribution for Type II censored data”, Computational Statistics & Data Analysis, 54(6): 1547-1558, (2010). ‏ ‏ [28] Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E., “Equation of state calculations by fast computing machines”, The journal of chemical physics, 21(6): 1087-1092, (1953).‏
  • [29] Nassar, M., Abo-Kasem, O., Zhang, C., & Dey, S., “Analysis of Weibull Distribution Under Adaptive Type II Progressive Hybrid Censoring Scheme”, Journal of the Indian Society for Probability and Statistics, 19(1): 25-65, (2018).
  • [30] Almetwally, E. M., Almongy, H. M., & El sayed Mubarak, A., “Bayesian and Maximum Likelihood Estimation for the Weibull Generalized Exponential Distribution Parameters Using Progressive Censoring Schemes”, Pakistan Journal of Statistics and Operation Research, 14(4): 853-868, (2018).‏
  • [31] Smith, R. L., & Naylor, J., “A comparison of maximum likelihood and Bayesian estimators for the three‐parameter Weibull distribution”, Journal of the Royal Statistical Society: Series C (Applied Statistics), 36(3): 358-369, (1987).
  • [32] Tahir, M. H., Cordeiro, G. M., Mansoor, M., & Zubair, M. “The Weibull-Lomax distribution: properties and applications”, Hacettepe Journal of Mathematics and Statistics, 44(2): 461-480, (2015). ‏
  • [33] Badar, M. G., & Priest, A. M., “Statistical aspects of fiber and bundle strength in hybrids composite”, Progress in Science and Engineering Composites, 1129–1136, (1982).
  • [34] Dey, S., Dey, T., & Kundu, D., “Two-parameter Rayleigh distribution: different methods of estimation”, American Journal of Mathematical and Management Sciences, 33(1): 55-74, (2014).
  • [35] Ijaz, M., & Asim, S. M., “Lomax exponential distribution with an application to real-life data”, PloS one, 14(12): e0225827, (2019).
  • [36] Hassan, A., & Mohamed, R., “Parameter Estimation for Inverted Exponentiated Lomax Distribution with Right Censored Data”, Gazi University Journal of Science, 32(4): 1370-1386, (2019).
  • [37] Hassan, A., Elshrpieby, E., & Mohamed, R., “Odd Generalized Exponential Power Function Distribution: Properties & Applications”, Gazi University Journal of Science, 32(1): 351-370, (2019).
  • [38] Alshenawy, R., Al-Alwan, A., Almetwally, E. M., Afify, A. Z., & Almongy, H. M., “Progressive type II censoring schemes of extended odd Weibull exponential distribution with applications in medicine and engineering”, Mathematics, 8(10): 1679, (2020).
  • [39] Alshenawy, R., Sabry, M. A., Almetwally, E. M., & Almongy, H. M., “Product Spacing of Stress–Strength under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution”, Computers, Materials & Continua, 66(3): 2973-2995, (2021).
  • [40] Muhammed, H. Z., & Almetwally, E. M., “Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution Under Progressive Type II Censoring”, Annals of Data Science, 1-32, (2020).
  • [41] El-Morshedy, M., Alhussain, Z. A., Atta, D., Almetwally, E. M., & Eliwa, M. S., “Bivariate Burr X generator of distributions: properties and estimation methods with applications to complete and type II censored samples”, Mathematics, 8(2): 264, (2020).
There are 41 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Statistics
Authors

Ehab M. Almetwally 0000-0002-3888-1275

Publication Date March 1, 2022
Published in Issue Year 2022 Volume: 35 Issue: 1

Cite

APA Almetwally, E. M. (2022). Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring. Gazi University Journal of Science, 35(1), 293-312. https://doi.org/10.35378/gujs.741755
AMA Almetwally EM. Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring. Gazi University Journal of Science. March 2022;35(1):293-312. doi:10.35378/gujs.741755
Chicago Almetwally, Ehab M. “Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation Based on Type I and Type II Censoring”. Gazi University Journal of Science 35, no. 1 (March 2022): 293-312. https://doi.org/10.35378/gujs.741755.
EndNote Almetwally EM (March 1, 2022) Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring. Gazi University Journal of Science 35 1 293–312.
IEEE E. M. Almetwally, “Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring”, Gazi University Journal of Science, vol. 35, no. 1, pp. 293–312, 2022, doi: 10.35378/gujs.741755.
ISNAD Almetwally, Ehab M. “Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation Based on Type I and Type II Censoring”. Gazi University Journal of Science 35/1 (March 2022), 293-312. https://doi.org/10.35378/gujs.741755.
JAMA Almetwally EM. Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring. Gazi University Journal of Science. 2022;35:293–312.
MLA Almetwally, Ehab M. “Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation Based on Type I and Type II Censoring”. Gazi University Journal of Science, vol. 35, no. 1, 2022, pp. 293-12, doi:10.35378/gujs.741755.
Vancouver Almetwally EM. Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring. Gazi University Journal of Science. 2022;35(1):293-312.

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