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您的检索式：作者名="Ahmed Z.Afify"
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| 1 | Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution显示文摘The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution.The considered family includes various asymmetrical and symmetrical probability distributions as special cases.A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution.Key statistical properties of this distribution including quantile,mean residual life,order statistics and moments are derived.The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods.A simulation study which provides asymptotic distribution of all considered point estimators,90%and 95%asymptotic confidence intervals are performed to examine the estimation efficiency of the considered methods numerically.The simulation results show that both biases and variances of the estimators tend to zero as the sample size increases,i.e.,the estimators are asymptotically consistent.Also,when the sample size increases the coverage probabilities of the confidence intervals increase to the nominal levels,while the corresponding length decrease and approach zero.Two real data sets from the medicine filed are used to illustrate the flexibility of the Arcsine–Gaussian distribution as compared with the normal,logistic,and Cauchy models.The proposed distribution is very versatile to fit real applications and can be used as a good alternative to the traditional gaussian distribution. | Farouq Mohammad A.Alam Sharifah Alrajhi Mazen Nassar Ahmed Z.Afify | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 2 | Modeling COVID-19 Pandemic Dynamics in Two Asian Countries显示文摘The current epidemic outbreak COVID-19 first took place in the Wuhan city of China and then spread worldwide.This deadly disease affected millions of people and compelled the governments and other concerned institutions to take serious actions.Around 0.28 million people have died from the COVID-19 outbreak as of May 11,2020,05:41 GMT,and the number is still increasing exponentially.The results of any scientific investigation of this phenomenon are still to come.However,now it is urgently needed to evaluate and compare the disease dynamics to improve the quarantine activities and the level of individual protection,to at least speed up the rate of isolation of infected persons.In the domain of big data science and other related areas,it is always of interest to provide the best description of the data under consideration.Therefore,in this article,we compare the COVID-19 pandemic dynamics between two neighboring Asian countries,Iran and Pakistan,to provide a framework to arrange the appropriate quarantine activities.Simple tools for comparing this deadly pandemic dynamic have been presented that can be adopted to produce the bases for inferences.Most importantly,a new statistical model is developed to provide the best description of COVID-19 daily deaths data in Iran and Pakistan. | Jin Zhao Zubair Ahmad Zahra Almaspoor M.El-Morshedy Ahmed Z.Afify | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 3 | The Lambert-G Family:Properties,Inference,and Applications显示文摘This study proposes a new flexible family of distributions called the Lambert-G family.The Lambert family is very flexible and exhibits desirable properties.Its three-parameter special sub-models provide all significantmonotonic and non-monotonic failure rates.A special sub-model of the Lambert family called the Lambert-Lomax(LL)distribution is investigated.General expressions for the LL statistical properties are established.Characterizations of the LL distribution are addressed mathematically based on its hazard function.The estimation of the LL parameters is discussed using six estimation methods.The performance of this estimation method is explored through simulation experiments.The usefulness and flexibility of the LL distribution are demonstrated empirically using two real-life data sets.The LL model better fits the exponentiated Lomax,inverse power Lomax,Lomax-Rayleigh,power Lomax,and Lomax distributions. | Jamal N.Al Abbasi Ahmed Z.Afify Badr Alnssyan Mustafa S.Shama | 2024 | Computer Modeling in Engineering & Sciences2024，140，7： | 0 |
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