ملف المستخدم
صورة الملف الشخصي

حازم اسماعيل الشيخ احمد

إرسال رسالة

التخصص: استاذ مشارك في الاحصاء الرياضي

الجامعة: جامعة عين شمس القاهرة

النقاط:

11
معامل الإنتاج البحثي

الخبرات العلمية

الأبحاث المنشورة

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: Pakistan Journal of Statistics

سنة النشر: 2021

تاريخ النشر: 2021-01-01

In this paper, the generalized order statistics (GOS) pertaining to Burr III distribution has been considered. The joint and the conditional pdf and cdf of the GOS for the Burr III distribution is studied within a limited scope. In addition, the current paper seeks to spot the light on some special forms derived through the mentioned joint and conditional pdf and cdf of the GOS for the Burr III.

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: Journal of Applied Probability and Statistics

سنة النشر: 2019

تاريخ النشر: 2019-04-01

Background: Outlier detection has recently become an important problem in many industrial and financial applications. The proposal in this paper is based on detect an outlier in circular data by the local density factor (LDF). The name of local density estimate (LDE) is justified by the fact that we sum over a local neighborhood compared to the sum over the whole circular data commonly used to compute the kernel density estimate (KDE). Methods: We discuss new techniques for outlier detection which find the outliers by comparing the local density of each point to the local density of its neighbors in circular data. In our experiments, we performed simulated two data sets generated a set of circular random variables from von Mises distribution with different sizes and each have two clusters non-uniform density and sizes, then we used (LDF) algorithm. Results: The results show that (LDF) algorithm detect an outliers in five samples named as A, B, C, D and E using von Mises concentration parameter (k( and suitable smoothing parameter (h) for two different datasets. Conclusion: It can be concluded from the present study that the proposed method (LDF method) can be very successful for the outlier detection task in circular data.

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: IUG Journal of Natural Studies

سنة النشر: 2017

تاريخ النشر: 2017-08-01

In this paper, we use the adaptive kernel estimates method to improve nonparametically the estimator of the probability density function (pdf) using the Erlang kernel (Erlang estimator). In addition, the cumulative distribution (cdf) of the improved Erlang estimator and the related hazard rate function for independent and identically distributed (iid) data will be evaluated. The performance of improved Erlang estimator and the related hazard rate function are tested using a simulation study.

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: IUG Journal of Natural Studies

سنة النشر: 2017

تاريخ النشر: 2017-08-01

In this paper, we use the adaptive kernel estimates method to improve nonparametically the estimator of the probability density function (pdf) using the Erlang kernel (Erlang estimator). In addition, the cumulative distribution (cdf) of the improved Erlang estimator and the related hazard rate function for independent and identically distributed (iid) data will be evaluated. The performance of improved Erlang estimator and the related hazard rate function are tested using a simulation study.

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: IUG Journal of Natural Studies

سنة النشر: 2017

تاريخ النشر: 2017-08-01

In this paper, we use the adaptive kernel estimates method to improve nonparametically the estimator of the probability density function (pdf) using the Erlang kernel (Erlang estimator). In addition, the cumulative distribution (cdf) of the improved Erlang estimator and the related hazard rate function for independent and identically distributed (iid) data will be evaluated. The performance of improved Erlang estimator and the related hazard rate function are tested using a simulation study.

EXPLICIT FORMS OF GENERALIZED ORDER STATISTICS OF BURR III DISTRIBUTION

المجلة: IUG Journal of Natural Studies

سنة النشر: 2017

تاريخ النشر: 2017-08-01

In this paper, we use the adaptive kernel estimates method to improve nonparametically the estimator of the probability density function (pdf) using the Erlang kernel (Erlang estimator). In addition, the cumulative distribution (cdf) of the improved Erlang estimator and the related hazard rate function for independent and identically distributed (iid) data will be evaluated. The performance of improved Erlang estimator and the related hazard rate function are tested using a simulation study.