Browsing by Author "Hussam, E"

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    Lomax tangent generalized family of distributions: Characteristics, simulations, and applications on hydrological-strength data
    (2024-05) Zaidi, S; Mahmood, Z; Atchadé, M; Tashkandy, Y; Bakr, M; Almetwally, E; Hussam, E; Gemeay, A; Kumar, A
    This article proposes and discusses a novel approach for generating trigonometric G-families using hybrid generalizers of distributions. The proposed generalizer is constructed by utilizing the tangent trigonometric function and distribution function of base model 𝐺(𝑥). The newly proposed family of uni-variate continuous distributions is named the “Lomax Tangent Generalized Family of Distributions (LT-G)” and structural-mathematical-statistical properties are derived. Some special and sub-models of the proposed family are also presented. A Weibull-based model, ‘The Lomax Tangent Weibull (LT-W) Distribution,” is discussed and the plots of density (pdf) and hazard (hrf) functions are also explained. Model parameter estimates are estimated by employing the maximum likelihood estimation (MLE) procedure. The accuracy of the MLEs is evaluated through Monte Carlo simulation. Last but not least, to demonstrate the flexibility and potential of the proposed distribution, two actual hydrological and strength data sets are analyzed. The obtained results are compared with well-known, competitive, and related existing distributions.
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    Mean estimation using an efficient class of estimators based on simple random sampling: Simulation and applications
    (2024-02) Kumar, A; Siddiqui, A; Mustafa, M; Hussam, E; Aljohani, H; Almulhim, F
    In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean 𝑌̄ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators.