2021
DOI: 10.3390/sym13112064
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An Alternate Generalized Odd Generalized Exponential Family with Applications to Premium Data

Abstract: In this article, we use Lehmann alternative-II to extend the odd generalized exponential family. The uniqueness of this family lies in the fact that this transformation has resulted in a multitude of inverted distribution families with important applications in actuarial field. We can characterize the density of the new family as a linear combination of generalised exponential distributions, which is useful for studying some of the family’s properties. Among the structural characteristics of this family that a… Show more

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Cited by 10 publications
(6 citation statements)
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“…The first data set, referred to as Data 1, examines the most prevalent grievances against automobile insurance companies during a two-year period as a percentage of their total business, including non-renewal of insurance and no fault claims. This data set was also considered in [36].…”
Section: Simulation Analysismentioning
confidence: 99%
“…The first data set, referred to as Data 1, examines the most prevalent grievances against automobile insurance companies during a two-year period as a percentage of their total business, including non-renewal of insurance and no fault claims. This data set was also considered in [36].…”
Section: Simulation Analysismentioning
confidence: 99%
“…Stochastic ordering is another important tool in statistics to define the comparative behaviour specifically in reliability theory. Suppose the two rvs , say X 1 and X 2 and under specific circumstance; let us consider that rv X 1 is lower than X 2 ; the readers can refer to the work of Khan et al [ 21 ] for detailed illustration on four stochastic ordering and their well-established relationships. …”
Section: Properties Of the Ebell-g Familymentioning
confidence: 99%
“…The third data deal with upheld most frequent complaints such as nonrenewal of insurance, and no fault claims commonly against vehicle insurance company over two-year period as a proportion of their overall business. The dataset was also used by Khan et al [ 21 ]. The descriptive summary of all three datasets is shown in Table 7 and consists of sample size n , minimum claim x 0 , maximum claim x n , lower Q 1 and upper Q 3 , quartile deviations, mean , median , standard deviation σ , measures of skewness S k , and kurtosis K .…”
Section: Practical Implementation Of the Proposed Ebelle Modelmentioning
confidence: 99%
“…Many authors have suggested new generators or families in the literature, for example, and not exclusively: Marshall and Olkin (1997) [1] introduced the Marshall-Olkin family, Gupta et al (1998) [2] introduced the exponentiated-G family, Eugene et al (2002) [3] proposed the beta-G family, Cordeiro and Castro (2011) [4] suggested the Kumaraswamy-G family, Alexander et al (2012) [5] presented the McDonald-G family, Alzaatreh et al (2013) [6] proposed the transformed-transformer (T-X) family, Bourguignon et al (2014) [7] presented the Weibull-G family, Tahir et al (2015) [8] studied the odd generalized exponential-G family, Cordeiro et al (2016) [9] discussed the Zografos Balakrishnan odd log-logistic family, Gomes-Silva et al (2017) [10] presented the odd Lindley-G family, Alizadeh et al (2017) [11] provided the Gompertz-G family and Jamal et al (2017) [12] defined the odd Burr-III family, among others. For a clearer understanding of the odds ratio to define new G-classes, we motivate the readers to Khan et al (2021) [13], in which the authors adopted a unique odd function to propose an alternate generalized odd generalized exponential-G family.…”
Section: Introductionmentioning
confidence: 99%