A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications
This study introduces the NGLXT-E, a novel probability distribution derived from the Logistic-X family, designed to enhance flexibility and robustness in modeling datasets with extreme skewness and heavy tails. The distribution excels in survival analysis, reliability engineering, and financial risk...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2025-03-01
|
| Series: | Research in Statistics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/27684520.2025.2477232 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850272295891763200 |
|---|---|
| author | Okechukwu J. Obulezi Happiness O. Obiora-Ilouno George A. Osuji Mohamed Kayid Oluwafemi Samson Balogun |
| author_facet | Okechukwu J. Obulezi Happiness O. Obiora-Ilouno George A. Osuji Mohamed Kayid Oluwafemi Samson Balogun |
| author_sort | Okechukwu J. Obulezi |
| collection | DOAJ |
| description | This study introduces the NGLXT-E, a novel probability distribution derived from the Logistic-X family, designed to enhance flexibility and robustness in modeling datasets with extreme skewness and heavy tails. The distribution excels in survival analysis, reliability engineering, and financial risk management, outperforming established models. Objectives include defining the new family, deriving properties, analyzing a special sub-model, and developing parameter estimation methods under an uncensored sample. Applications involve diverse datasets, such as HIV/AIDS death rates in Germany (2000–2020), infection times for kidney dialysis patients, failure times of repairable items, and Bitcoin trading volumes (2014–2024). The NGLXT-E distribution demonstrates a superior fit over existing models like the generalized inverted exponential and Weibull distributions, assessed via statistical criteria such as the Kolmogorov-Smirnov test, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Additionally, Bitcoin’s volatility was modeled using an exponential GARCH (eGARCH) framework, validating the NGLXT-E distribution’s applicability to financial data. This research significantly contributes to statistical literature by proposing a flexible new family of distributions, advancing parameter inference techniques, and demonstrating practical superiority across real-world datasets. |
| format | Article |
| id | doaj-art-2b57483fc6f74d0d9cf2d64b32fbb34e |
| institution | OA Journals |
| issn | 2768-4520 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Taylor & Francis |
| record_format | Article |
| series | Research in Statistics |
| spelling | doaj-art-2b57483fc6f74d0d9cf2d64b32fbb34e2025-08-20T01:51:53ZengTaylor & FrancisResearch in Statistics2768-45202025-03-013110.1080/27684520.2025.2477232A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applicationsOkechukwu J. Obulezi0Happiness O. Obiora-Ilouno1George A. Osuji2Mohamed Kayid3Oluwafemi Samson Balogun4Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, NigeriaDepartment of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, NigeriaDepartment of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, NigeriaDepartment of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Computing, University of Eastern Finland, Kuopio, FinlandThis study introduces the NGLXT-E, a novel probability distribution derived from the Logistic-X family, designed to enhance flexibility and robustness in modeling datasets with extreme skewness and heavy tails. The distribution excels in survival analysis, reliability engineering, and financial risk management, outperforming established models. Objectives include defining the new family, deriving properties, analyzing a special sub-model, and developing parameter estimation methods under an uncensored sample. Applications involve diverse datasets, such as HIV/AIDS death rates in Germany (2000–2020), infection times for kidney dialysis patients, failure times of repairable items, and Bitcoin trading volumes (2014–2024). The NGLXT-E distribution demonstrates a superior fit over existing models like the generalized inverted exponential and Weibull distributions, assessed via statistical criteria such as the Kolmogorov-Smirnov test, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Additionally, Bitcoin’s volatility was modeled using an exponential GARCH (eGARCH) framework, validating the NGLXT-E distribution’s applicability to financial data. This research significantly contributes to statistical literature by proposing a flexible new family of distributions, advancing parameter inference techniques, and demonstrating practical superiority across real-world datasets.https://www.tandfonline.com/doi/10.1080/27684520.2025.2477232New generalized Logistic-X transformed familymaximum likelihood estimationHIV/AIDS mortality ratesBitcoin-USD trading dataeGARCH model |
| spellingShingle | Okechukwu J. Obulezi Happiness O. Obiora-Ilouno George A. Osuji Mohamed Kayid Oluwafemi Samson Balogun A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications Research in Statistics New generalized Logistic-X transformed family maximum likelihood estimation HIV/AIDS mortality rates Bitcoin-USD trading data eGARCH model |
| title | A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications |
| title_full | A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications |
| title_fullStr | A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications |
| title_full_unstemmed | A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications |
| title_short | A new family of generalized distributions based on logistic-x transformation: sub-model, properties and useful applications |
| title_sort | new family of generalized distributions based on logistic x transformation sub model properties and useful applications |
| topic | New generalized Logistic-X transformed family maximum likelihood estimation HIV/AIDS mortality rates Bitcoin-USD trading data eGARCH model |
| url | https://www.tandfonline.com/doi/10.1080/27684520.2025.2477232 |
| work_keys_str_mv | AT okechukwujobulezi anewfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT happinessoobiorailouno anewfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT georgeaosuji anewfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT mohamedkayid anewfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT oluwafemisamsonbalogun anewfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT okechukwujobulezi newfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT happinessoobiorailouno newfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT georgeaosuji newfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT mohamedkayid newfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications AT oluwafemisamsonbalogun newfamilyofgeneralizeddistributionsbasedonlogisticxtransformationsubmodelpropertiesandusefulapplications |