On the Genesis of the Marshall-Olkin Family of Distributions via the T-X Family Approach: Statistical Modeling
Source abstract
Abstract
In the last couple of years, there Has been an increased interest among the statisticians to define new families of distributions by adding one or more additional parameter(s) to the baseline distribution. In this regard, a number of families have been introduced and studied. One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions. Whenever, we see a new method, the natural questions come in to mind are (i) what are the genesis of the newly proposed method and (ii) how did the proposed method is obtained. No doubt, the Marshall-Olkin family is a very useful method and has attracted the researchers. But, unfortunately, the authors failed to provide the explanation about the genesis of the method that how this family of distributions is obtained. To address this issue, in this article, an attempt Has been made to provide a straight forward computation about the genesis of the Marshall-Olkin family that somehow completes its derivation. The genesis of the Marshall-Olkin family is based on the T-X family approach. Furthermore, we have showed that other extensions of the Marshall-Olkin family can also be obtained via the T-X family method. Finally, a real-life application form insurance science is presented to illustrate the newly proposed extension of the Marshall-Olkin family.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Statistical Distribution Estimation and Applications · Financial Risk and Volatility Modeling · Probabilistic and Robust Engineering Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (18)
Show source-provided references
1. Generalized inverse lindley distribution with application to Danish fire insurance data10.1080/03610926.2015.1096394 ↗
2. On generalized log-moyal distribution: A new heavy tailed size distribution
3. How do risk attitudes of clearing firms matter for managing default exposure in futures markets?10.1080/1351847X.2015.1041148 ↗
4. A comparison between skew-logistic and skew-normal distributions10.11113/matematika.v31.n1.742 ↗
5. Asymptotic ruin probabilities for a bivariate Lévy-driven risk model with heavy-tailed claims and risky investments10.1239/jap/1354716649 ↗
6. Modeling vehicle insurance loss data using a new member of TX family of distributions10.2991/jsta.d.200421.001 ↗
7. New methods to define heavy-tailed distributions with applications to insurance data10.1080/16583655.2020.1741942 ↗
8. A new flexible bathtub-shaped modification of the Weibull model: Properties and applications
9. A new lifetime exponential-X family of distributions with applications to reliability data10.1155/2020/1316345 ↗
10. A new modified Weibull distribution10.1016/j.ress.2012.10.018 ↗
11. A review on the accuracy of bladder cancer detection methods10.7150/jca.28989 ↗
12. The power Lomax distribution with an application to bladder cancer data10.1186/s40064-016-3464-y ↗
13. Application of the Weibull distribution with a non-constant shape parameter for identifying risk factors in pharyngeal cancer patients
14. Application of Weibull model for survival of patients with gastric cancer10.1186/1471-230X-11-1 ↗
15. A flexible reduced logarithmic-X family of distributions with biomedical analysis
16. Compounding of distributions: A survey and new generalized classes10.1186/s40488-016-0052-1 ↗
17. Recent developments in distribution theory: A brief survey and some new generalized classes of distributions10.18187/pjsor.v15i1.2803 ↗
18. A new method for generating families of continuous distributions10.1007/s40300-013-0007-y ↗