MOHAMED ABDELAAL MOSTAFA ELKORASHY Lecturer at AASTMT

biography

•Assistant Researcher Mathematics department, Faculty of Science, Tanta University (2010). •Statistical Consultant and data analyzer (2009 – Now). •Teaching Assistant SPSS training for 3rd year students Faculty of Science, Tanta University (2009). •Member in the team work of Management Information System (MIS) project Tanta University (2007).

Education

2024

Doctorate - Faculty of science-Tanta University - Egypt

Abstract The use of probability distributions is essential for accurately modeling all types of ‎datasets in numerous fields, such as economics, engineering, social sciences, ‎health, and biological sciences. The parameterization of probability distributions ‎enables more adaptable models that better reflect the properties of the underlying ‎data. With the growing amount of available data for analysis, new probabilistic ‎distributions are needed to better describe each phenomenon or experiment. The ‎advent of computer-based tools now allows for the use of more complex ‎distributions with a larger number of parameters, making it possible to study larger ‎amounts of data. ‎ Many distributions have been developed and applied, with one or more shape ‎parameters that enable them to take on a variety of shapes. Induction of these ‎parameters has been shown to improve the goodness-of-fit of new distributions, ‎which are particularly useful in modeling applications and can better fit skewed data ‎than existing distributions. The literature in this field presents various methods for ‎modifying continuous distributions, each with the potential to generate a probability ‎distribution that provides a better fit for a given dataset. ‎ This thesis covers most of these methods and is divided into four chapters.‎ Chapter 1 is introductory in nature. At the very outset, it is important to ensure that ‎the thesis can be a comprehensive, standalone research piece. This chapter ‎provides an overview of the basic definitions, main concepts, and estimation ‎methods that are used throughout the thesis. It includes a summary of point ‎estimation, its properties, and methods. The goal of this chapter is to provide a ‎foundation for the reader to understand the subsequent chapters of the thesis.‎ Chapter 2 provides a general introduction to circular statistics and a survey of ‎recent scientific literature, along with the objectives of the study. It introduces and ‎examines three distributions, namely the wrapped lognormal distribution, the ‎wrapped logistic distribution, and the wrapped weibull distribution. The chapter ‎presents their characteristic function, density function, and distribution function, ‎and derives trigonometric moments and related parameters.‎ In Chapter 3, we introduce the wrapped Monsef distribution (WM), a novel ‎probability distribution. We derive its characteristic function, density function, and ‎distribution function, and compute its trigonometric moments and related ‎parameters. We establish its divisibility properties, including infinite divisibility, ‎geometric infinite divisibility, and characterization of the density. ‎ We estimate the model parameters using both characteristic functions and the ‎method of moments and investigate the density function's behavior under various ‎parameter values. Additionally, we obtain expressions for the characteristic ‎function, trigonometric moments, and related descriptive measures. The maximum ‎likelihood estimation method is employed to estimate the model parameters, and ‎we conduct a simulation analysis to demonstrate the accuracy of the resulting ‎estimator. Finally, we apply the suggested model to three real-life datasets and ‎compare its flexibility and performance with that of other wrapped distributions.‎ The significance of reliability has grown considerably in communication technology ‎and industrial fields due to automation and complex mechanization, leading to ‎specializations in mechanical, software, robot, and medical device reliability. ‎Reliability is defined as the probability of a system performing a particular function ‎under specific environmental and operational conditions over a specified time. ‎Stress-strength reliability refers to R=P(Y

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Teaching Courses

Course	Academic year	Term
BA327 - Statistics and Numerical Methods	2014	Fall Semester	View All Content

EBA110+ABA110 - Mathematics	2014	Fall Semester	View All Content

EB127 - Mathematics	2013	Fall Semester	View All Content

EA213 - Statistics	2013	Fall Semester	View All Content

EB128 - Mathematics in business	2013	Fall Semester	View All Content

BA203 - Probability and Statistics	2012	Fall Semester	View All Content

BA326 - Mathematics 6 Probability and Statistics	2012	Fall Semester	View All Content

BA329 - Probability and Statistics	2012	Fall Semester	View All Content

BA102 - Calculus 2	2012	Fall Semester	View All Content

BA124 - Mathematics II	2011	Fall Semester	View All Content

BA110 - Mathematics (Logistics)	2011	Fall Semester	View All Content

BA123 - Mathematics I	2011	Fall Semester	View All Content

BA020 - Preparatory Mathematics	2010	Fall Semester	View All Content

BA121 - Mathematics 1	2010	Fall Semester	View All Content

BA122 - Mathematics 2 Geometric and vector analysis	2010	Fall Semester	View All Content

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Branch : AASTMT AbuKir Branch, Alexandria, Egypt

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Mobile : 01003348433

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Location : AbuKir G 112G

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