د. سميةالجابري

قسم الرياضيات كلية التربية قصر بن غشير

الاسم الكامل

د. سمية صالح محمد الجابري

المؤهل العلمي

دكتوراة

الدرجة العلمية

محاضر

ملخص

سمية الجابري هي احد اعضاء هيئة التدريس بقسم الرياضيات بكلية التربية قصر بن غشير. تعمل السيدة سمية الجابري بجامعة طرابلس كـمحاضر منذ 2013-10-27 ولها العديد من المنشورات العلمية في مجال تخصصها

تنزيل السيرة الذاتية

معلومات الاتصال

روابط التواصل

الإستشهادات

الكل منذ 2017
الإستشهادات
h-index
i10-index

المؤهلات

دكتوراة

نظرية القيم الشادة- الاحصاء
جامعة مانشستر/ المملكة المتحدة
4 ,2013

ماجستير

الاحصاء الرياضي
كلية العلوم / جامعة طرابلس
6 ,2005

بكالوريوس

الاحصاء
كلية العلوم جامعة طرابلس
2 ,2000

المنشورات

The kumaraswamy gp distribution

The generalized Pareto (GP) distribution is the most popular model for extreme values. Recently, Papastathopoulos and Tawn [Journal of Statistical Planning and Inference 143 (2013), 131-143] have proposed some generalizations of the GP distribution for improved modeling. Here, we point that Papastathopoulos and Tawn’s generalizations are in fact not new and then go on to propose a tractable generalization of the GP distribution. For the latter generalization, we provide a comprehensive treatment of mathematical properties, estimate parameters by the method of maximum likelihood and provide the observed information matrix. The proposed model is shown to give a better fit for the real data set used in Papastathopoulos and Tawn.
sumaya saleh eljabri(8-2013)
full text Publisher's website


On Chen et al.’s Extreme Value Distribution

Chen, Bunce and Jiang [In: Proceedings of the International Conference on Computational Intelligence and Software Engineering, pp. 1-4] claim to have proposed a new extreme value distribution. But the formulas given for the distribution do not form a valid probability distribution. Here, we correct their formulas to form a valid probability distribution. For this valid distribution, we provide a comprehensive treatment of mathematical properties, estimate parameters by the method of maximum likelihood and provide the observed information matrix. The flexibility of the distribution is illustrated using a real data set
sumaya saleh eljabri(8-2014)
full text Publisher's website


Empirical Distribution of the Sample Mean Based on Uniform (0, 1) Random Samples

The Uniform distribution on the interval (0, 1) plays an important role in many statistical applications, such as, its role in simulation procedures when a sequence of random numbers needs to be generated from some parent population. This paper investigates the sampling distribution of the sample mean for random samples drawn from a uniform (0, 1) population. A complete derivation of the probability density function of the sample mean is presented. The normal approximation to the series form of the probability density function of the sample mean is also discussed. To avoid the use of the complicated series form of the probability density function of the sample mean for small sample sizes when the normal approximation is not advisable, the tables of the cumulative distribution function for sample sizes 2, 3, 4 and 5 are constructed. The Minitab statistical software is used throughout this paper
sumaya saleh eljabri, Yousef M. Emhemmed(1-2013)
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The Kumaraswamy GEV distribution

for extreme values in many areas. We propose a generalization – referred to as the Kumaraswamy GEV distribution – and provide a comprehensive treatment of its mathematical properties. We estimate its parameters by the method of maximum likelihood and provide the observed information matrix. An application to some real data illustrates flexibility of the new model. Finally, some bivariate generalizations of the model are proposed.
sumaya saleh eljabri, saralees nadarajah(10-2017)
Publisher's website