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Iman Mohamed Attia

Publications and source records attributed to Iman Mohamed Attia.

6 recordsLinked to original sources

A new six parameters generalized Weibull-Kumaraswamy-{Lomax) Distribution with a Novel Linear Series Expansion of Its Probability Density

One method of generalizing a distribution is to add new parameter to the baseline distribution. In this paper the author discusses this methodology to create a new six parameters generalized Weibull-Kumaraswamy-{Lomax) Distribution. The author utilizes the approach of the T-X{Y} family to add new parameters the baseline distribution Kumaraswamy distribution defined on the unit interval with two shape parameters a & b>0. The author used the Lomax distribution with two parameters α & λ>0 , scale and shape parameters respectively, to link the baseline distribution with the generator distribution. The generator distribution is the Weibull distribution with the two parameters, c & γ>0 , shape and scale parameters respectively as will be explained in the paper. The result is a new distribution with six parameters. The author derived the PDF, CDF, and quantile functions. The author used a new technique to expand the pdf in a linear series expansion that is simple and does not require special function. This makes the computational statistics for the raw moments easier and simpler. The author discusses the Shannon entropy and the paper contains figures for the PDF, CDF and the quantile functions.

math.ST↗

Proxy Variable in OECD Database: Application of Parametric Quantile Regression and Median Based Unit Rayleigh Distribution

This paper presents an in-depth exploration of the innovative Median-based unit Rayleigh (MBUR) distribution, previously introduced by the author. This new approach is specifically designed for conducting quantile regression analysis, enabling researchers to gain valuable insights into real-world data applications. The author effectively demonstrates the feasible advantage of the MBUR distribution, highlighting its potential to connect advanced statistical theory with meaningful results in data analysis. The author utilized OECD data in employing the parametric MBUR quantile regression using the response variables which are distributed as MBUR.

stat.AP↗

New generalized unit distributions based on order statistics

In the present paper, the author discusses the derivation of unit distributions and the derivation of the generalized form using the order statistics. The author discusses the Kumaraswamy as the smallest order statistic of the unit power distribution derived from the inverse Weibull distribution. The author discusses the unit Rayleigh distribution and how it can be generalized using the smallest, largest, and kth order statistics. Using the order statistics to generalize a distribution differs from other techniques like the power transformation and T-X family (transformed-transformer) method. For the discussed distribution, the author demonstrates the basic functions and properties with real data analysis.

stat.ME↗

New Three Different Generators for Constructing New Three Different Bivariate Copulas

In this paper, the author introduces new methods to construct Archimedean copulas. The generator of each copula fulfills the sufficient conditions as regards the boundary and being continuous, decreasing, and convex. Each inverse generator also fulfills the necessary conditions as regards the boundary conditions, marginal uniformity, and 2-increasing properties. Although these copulas satisfy these conditions, they have some limitations. They do not cover the entire dependency spectrum, ranging from perfect negative dependency to perfect positive dependency, passing through the independence state

math.ST↗

Generalized Method of Moments and Percentile Method: Estimating parameters of the Novel Median Based Unit Weibull Distribution

The Median Based Unit Weibull is a new 2 parameter unit Weibull distribution defined on the unit interval (0,1). Estimation of the parameters using MLE encountered some problems like large variance. Using generalized method of moments (GMMs) and percentile method may ameliorate this condition. This paper introduces GMMs and the percentile methods for estimating the parameters of the new distribution with illustrative real data analysis.

stat.ME↗