Some estimation methods for mixture of extreme value distributions with simulation and application in medicine

Lone, Showkat Ahmad; Anwar, Sadia; Sindhu, Tabassum Naz; Jarad, Fahd

doi:10.1016/j.rinp.2022.105496

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…Moreover, it is noted from different research that fuzzy integration enhances the capability of basic variants in solving non-trivial real-life problems with vagueness and uncertainty. In addition, different estimation methods can also be incorporated with MOORA to strengthen its ability under a fuzzy environment [ [269] , [270] , [271] , [272] , [273] ]. The fuzzy MOORA papers have reached almost equal publications with the single MOORA method.…”

Section: Discussionmentioning

confidence: 99%

A historical review and analysis on MOORA and its fuzzy extensions for different applications

Singh,

Pathak,

Kumar

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

A historical review and analysis on MOORA and its fuzzy extensions for different applications

Singh,

Pathak,

Kumar

et al. 2024

Heliyon

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“…It is well documented that the NH model can be extensively investigated in many study domains. Noteworthy studies involving this model include Almetwally and Meraou [4], Korkmaz et al [16], Tahir et al [28], Lone et al [17], and Anum Shafiq et al [8]. The cdf and pdf of ME distribution are formulated respectively as…”

Section: Of 22mentioning

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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“…Data-driven statistical models are widely used in biological, medicine, social science and other fields due to the flexibility and feasibility of the method [ 15 , 16 ]. Especially in recent years, it has played an important role in simulating COVID-19 pandemic [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi’an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

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Some estimation methods for mixture of extreme value distributions with simulation and application in medicine

Cited by 9 publications

References 36 publications

A historical review and analysis on MOORA and its fuzzy extensions for different applications

A historical review and analysis on MOORA and its fuzzy extensions for different applications

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

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

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