A new generalized rayleigh distribution with analysis to big data of an online community

Shen, Zhongjie; Alrumayh, Amani; Ahmad, Zubair; Abu-Shanab, Reman; Mutairi, Maha Al -; Aldallal, Ramy

doi:10.1016/j.aej.2022.05.010

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…News is the only way to collect information about the surroundings and society. Reporters and organizations gather news occurring in society and identify valuable customer data [1]. News data processing plays a significant role in creating news that improves the trustworthiness among the users.…”

Section: Introductionmentioning

confidence: 99%

Research on Data News Propagation Path Based on the Big Data Algorithm

Hao

Liu

Wang

2022

International Transactions on Electrical Energy Systems

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News propagation originates from a person/location, dwelling with an event that grabs significance. News data propagation relies on telecommunication and big data for precise content distribution and mitigation of false news. Considering these factors, the event-dependent data propagation technique (EDPT) was introduced to improve the data precision. These data refer to the news information originating and propagating from digital media. The data analysis considers the external factors for fake information and precise projection medium for preventing multiviewed false circulations. In this technique, the liability of the information is analyzed using a linear pattern support vector classifier. The data modification and propagation changes are classified based on liability information across the circulation time. The SVM classifier identifies these two factors with close liability validation, preventing false data. The data accumulation and analysis rates for the abovementioned classifications are performed in the propagation process using the classifier hyperplane. This plane is updated from the previous propagation point from which the events are identified. The proposed technique’s performance is analyzed using propagation accuracy, precision, false rate, time, and rate.

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Section: Introductionmentioning

confidence: 99%

Research on Data News Propagation Path Based on the Big Data Algorithm

Hao

Liu

Wang

2022

International Transactions on Electrical Energy Systems

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“…Cordeiro et al [17] studied the betageneralized Rayleigh distribution and its application. More generalizations of Rayleigh distribution can be found in the literature and one may refer to [18][19][20][21][22][23][24].…”

Section: Model Descriptionmentioning

confidence: 99%

Tampered Random Variable Analysis in Step-Stress Testing: Modeling, Inference, and Applications

Ahmad,

Ramadan,

Almetwally

2024

Mathematics

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This study explores a new dimension of accelerated life testing by analyzing competing risk data through Tampered Random Variable (TRV) modeling, a method that has not been extensively studied. This method is applied to simple step-stress life testing (SSLT), and it considers multiple causes of failure. The lifetime of test units under changeable stress levels is modeled using Power Rayleigh distribution with distinct scale parameters and a constant shape parameter. The research introduces unique tampering coefficients for different failure causes in step-stress data modeling through TRV. Using SSLT data, we calculate maximum likelihood estimates for the parameters of our model along with the tampering coefficients and establish three types of confidence intervals under the Type-II censoring scheme. Additionally, we delve into Bayesian inference for these parameters, supported by suitable prior distributions. Our method’s validity is demonstrated through extensive simulations and real data application in the medical and electrical engineering fields. We also propose an optimal stress change time criterion and conduct a thorough sensitivity analysis.

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“…For more information, refer to [29][30][31]. In this work, we are interested in studying a new form of the Rayleigh distribution called a new generalized Rayleigh distribution (NGR), which was first introduced by Shen et al [32]. It has three parameters and it was shown that the NGR is suitable for modeling large data values rather than small data values.…”

Section: Model Descriptionmentioning

confidence: 99%

Evaluating the Discrete Generalized Rayleigh Distribution: Statistical Inferences and Applications to Real Data Analysis

Haj Ahmad,

Ramadan,

Almetwally

2024

Mathematics

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Various discrete lifetime distributions have been observed in real data analysis. Numerous discrete models have been derived from a continuous distribution using the survival discretization method, owing to its simplicity and appealing formulation. This study focuses on the discrete analog of the newly generalized Rayleigh distribution. Both classical and Bayesian statistical inferences are performed to evaluate the efficacy of the new discrete model, particularly in terms of relative bias, mean square error, and coverage probability. Additionally, the study explores different important submodels and limiting behavior for the new discrete distribution. Various statistical functions have been examined, including moments, stress–strength, mean residual lifetime, mean past time, and order statistics. Finally, two real data examples are employed to evaluate the new discrete model. Simulations and numerical analyses play a pivotal role in facilitating statistical estimation and data modeling. The study concludes that the discrete generalized Rayleigh distribution presents a notably appealing alternative to other competing discrete distributions.

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A new generalized rayleigh distribution with analysis to big data of an online community

Cited by 13 publications

References 30 publications

Research on Data News Propagation Path Based on the Big Data Algorithm

Research on Data News Propagation Path Based on the Big Data Algorithm

Tampered Random Variable Analysis in Step-Stress Testing: Modeling, Inference, and Applications

Evaluating the Discrete Generalized Rayleigh Distribution: Statistical Inferences and Applications to Real Data Analysis

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