Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims

Meraou, Mohammed Amine; Al-Kandari, Noriah M.; Raqab, Mohammad Z.; Kundu, Debasis

doi:10.1080/00949655.2021.1981324

Cited by 16 publications

(4 citation statements)

References 20 publications

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“…By solving Equation (10), we can obtain a closed form of the MLE of β, β, which ensures that it exists and it is unique. It can be written as…”

Section: Estimation Based On Maximum Likelihood Methodsmentioning

confidence: 99%

“…In the context of distribution theory, the compound method is one of the most popular choices for fitting several types of datasets, such as skewed and lifetime data. It has been used in numerous domains of studies including economic, biology, actuarial, and environmental (see Abdelghani et al [7], Meraou et al [8][9][10][11], and Jafari and Tahmasebi [10]). The compound distributions are defined as the minimum or maximum of M independent and identically distributed (i.i.d) RVs.…”

Section: Introductionmentioning

confidence: 99%

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Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

Alghamdi,

Meraou,

Aljohani

et al. 2024

Symmetry

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The creating of novel models essentially stems from the requirement to appropriate describe survival cases. In this study, a novel lifetime model with two parameters is proposed and studied for modeling more types of data used in different study cases, including symmetric, asymmetric, skewed, and complex datasets. The proposed model is obtained by compounding the exponential and XLindley distributions, and it is regarded as a strong competitor for the widely applied symmetrical and non-symmetrical models. Several characteristics and statistical properties are investigated. The unknown parameters of the recommended model for the complete sample are estimated using two estimation methods; notably, maximum likelihood estimation and Bayes techniques based on several loss functions as well as an approximate tool are used to construct the confidence intervals for the unknown parameters of the suggested model. The estimation procedures are compared using a Monte Carlo simulation experiment to demonstrate their effectiveness. In the end, the applicability and flexibility of the recommended model are conducted using two real lifetime datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing distributions.

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“…By solving Equation (10), we can obtain a closed form of the MLE of β, β, which ensures that it exists and it is unique. It can be written as…”

Section: Estimation Based On Maximum Likelihood Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

Alghamdi,

Meraou,

Aljohani

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…This flexibility may be achieved by adding new factors to the classic distribution, including adding one or more parameters, or made several transformation to the baseline distribution, increasing its ability to handle complicated scenarios. In this context, numerous class of distributions have been obtained by applying different procedures to reinforce the adaptability of models, thereby creating a more versatile and potent distribution for dataset modeling, for example, see the studies of Madhavi and Kundu [18], Marshall and Olkin [19], Meraou and Raqab [23], Meraou el al. [24], Amal S. Hassan et al [6], Cordeiro et al [13], Bourguignon et al [10], Meraou et al [22], Zagrofos and Balakrishana [30], Eugene et al [14], Alzaatreh et al [5], Afify et al [2], and Meraou et al [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Version Three Parameter Nadarajah Haghighi Model: Entropy Measures, Inference and Applications

Alghamdi,

Meraou,

Aljohani

et al. 2024

Preprint

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The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences, notably lifetime, medical, and financial sciences. The Heavy tailed Nadarajah Haghighi model is introduced by compounding the heavy tailed family and Nadarajah Haghighi distribution in this paper. The so obtained model has three parameters accounting for the scale and shape of the distribution. The proposed distribution's fundamental characteristics such as probability density, cumulative distribution, hazard rate, and survival functions are provided, and several key statistical properties are established, and several entropy information measures are proposed. The estimation of model parameter is performed via the ML procedure. Further, different simulation experiments are conducted to demonstrate the performance of proposed estimator’s using some measure, like the average estimate, the average bias, and associated mean square error. Finally, we are applied our proposed model to analyze three different real datasets. In our illustration, we are compared the practicality of the recommended model with several well-known competing models.

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“…[2], Marshall and Olkin [3], Afify et al [4], Meraou and Raqab [5], Ahmad et al [6], Meraou el al. [7], Gemeay et al [8], Amal S. Hassan et al [9], Sapkota et al [10], Cordeiro et al [11], Teamah et al [12], Bourguignon et al [13], Yıldırım et al [14], Meraou et al [15], Almuqrin et al [16], Zagrofos and Balakrishana [17], Kamal et al [18], Teamah et al [19], Eugene et al [20], Alsadat et al [21], Alzaatreh et al [22], Nagy et al [23], and Meraou et al [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications

Alshawarbeh,

Alghamdi,

Meraou

et al. 2024

Symmetry

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The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences: notably, lifetime, medical, and financial sciences. This paper introduces a heavy-tailed Nadarajah Haghighi model by compounding the heavy-tailed family and Nadarajah Haghighi distribution. The model obtained has three parameters that account for the scale and shape of the distribution. The proposed distribution’s fundamental characteristics, such as the probability density, cumulative distribution, hazard rate, and survival functions, are provided, several key statistical properties are established, and several entropy information measures are proposed. Estimation of model parameters is performed via a maximum likelihood estimator procedure. Further, different simulation experiments are conducted to demonstrate the proposed estimator’s performance using measures like the average estimate, the average bias, and the associated mean square error. Finally, we apply our proposed model to analyze three different real datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing models.

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Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims

Cited by 16 publications

References 20 publications

Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

A Novel Version Three Parameter Nadarajah Haghighi Model: Entropy Measures, Inference and Applications

A Novel Three-Parameter Nadarajah Haghighi Model: Entropy Measures, Inference, and Applications

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