A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data
The main aim of the paper is to propose and study a new heavy-tail model for stochastic modeling under engineering data. After studying and analyzing its mathematical properties, different classical estimation methods such as the ordinary least square, Cramér-von Mises, weighted least square, maximu...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/1910909 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563058597167104 |
|---|---|
| author | M. El-Morshedy M. S. Eliwa Afrah Al-Bossly Haitham M. Yousof |
| author_facet | M. El-Morshedy M. S. Eliwa Afrah Al-Bossly Haitham M. Yousof |
| author_sort | M. El-Morshedy |
| collection | DOAJ |
| description | The main aim of the paper is to propose and study a new heavy-tail model for stochastic modeling under engineering data. After studying and analyzing its mathematical properties, different classical estimation methods such as the ordinary least square, Cramér-von Mises, weighted least square, maximum likelihood, and Anderson–Darling estimation along with its corresponding left-tail and right-tail estimation methods are considered. Comprehensive numerical simulation studies are performed for comparing estimation methods in terms of some criterions. Three engineering and medical real-life data sets are considered for measuring the applicability flexibility of the new model and to compare the competitive models under uncensored scheme. Two engineering real-life data of them are also used to compare the classical methods. A modified Nikulin-Bagdonavicius goodness-of-fit is presented and applied accordingly for validation under censorship case. Finally, right censored lymphoma data set is analyzed under the modified statistic test for checking the validation of the reciprocal Weibull model in modeling the right censored data. |
| format | Article |
| id | doaj-art-315e7654d0464cfda176309a7ac32667 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-315e7654d0464cfda176309a7ac326672025-02-03T01:21:06ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/1910909A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering DataM. El-Morshedy0M. S. Eliwa1Afrah Al-Bossly2Haitham M. Yousof3Department of MathematicsDepartment of Statistics and Operation ResearchDepartment of MathematicsDepartment of StatisticsThe main aim of the paper is to propose and study a new heavy-tail model for stochastic modeling under engineering data. After studying and analyzing its mathematical properties, different classical estimation methods such as the ordinary least square, Cramér-von Mises, weighted least square, maximum likelihood, and Anderson–Darling estimation along with its corresponding left-tail and right-tail estimation methods are considered. Comprehensive numerical simulation studies are performed for comparing estimation methods in terms of some criterions. Three engineering and medical real-life data sets are considered for measuring the applicability flexibility of the new model and to compare the competitive models under uncensored scheme. Two engineering real-life data of them are also used to compare the classical methods. A modified Nikulin-Bagdonavicius goodness-of-fit is presented and applied accordingly for validation under censorship case. Finally, right censored lymphoma data set is analyzed under the modified statistic test for checking the validation of the reciprocal Weibull model in modeling the right censored data.http://dx.doi.org/10.1155/2022/1910909 |
| spellingShingle | M. El-Morshedy M. S. Eliwa Afrah Al-Bossly Haitham M. Yousof A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data Journal of Mathematics |
| title | A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data |
| title_full | A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data |
| title_fullStr | A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data |
| title_full_unstemmed | A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data |
| title_short | A New Probability Heavy-Tail Model for Stochastic Modeling under Engineering Data |
| title_sort | new probability heavy tail model for stochastic modeling under engineering data |
| url | http://dx.doi.org/10.1155/2022/1910909 |
| work_keys_str_mv | AT melmorshedy anewprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT mseliwa anewprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT afrahalbossly anewprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT haithammyousof anewprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT melmorshedy newprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT mseliwa newprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT afrahalbossly newprobabilityheavytailmodelforstochasticmodelingunderengineeringdata AT haithammyousof newprobabilityheavytailmodelforstochasticmodelingunderengineeringdata |