Browsing by Author "Bakr, M"

Now showing 1 - 3 of 3
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    Design based synthetic imputation methods for domain mean
    (2024) Bhushan, S; Kumar, A; Phokhrel, R; Bakr, M; Mekiso, G
    In real life, situations may arise when the available data are insufcient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic efort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to frst order approximation. The efciency conditions are determined and a thorough simulation study is carried out using artifcially generated data sets. An application is included with real data that further supports this study.
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    Efficient imputation methods in case of measurement errors
    (2024-02) Kumar, A; Bhushan, S; Shukla, S; Bakr, M
    This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications
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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.