Mathematical Sciences

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Browsing Mathematical Sciences by Author "Albassam, Mohammed"

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    Estimation of reliability in a multicomponent stress–strength system for the exponentiated moment-based exponential distribution
    (Multidisciplinary Digital Publishing Institute, 2019) Rao, Srinivasa G.; Bhatti, Fiaz Ahmad; Aslam, Muhammad; Albassam, Mohammed
    A multicomponent system of k components with independent and identically distributed random strengths X 1, X 2 , … X k , with each component undergoing random stress, is in working condition if and only if at least s out of k strengths exceed the subjected stress. Reliability is measured while strength and stress are obtained through a process following an exponentiated moment-based exponential distribution with different shape parameters. Reliability is gauged from the samples using maximum likelihood (ML) on the computed distributions of strength and stress. Asymptotic estimates of reliability are compared using Monte Carlo simulation. Application to forest data and to breaking strengths of jute fiber shows the usefulness of the model.
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    Evaluation of bootstrap confidence intervals using a new non-normal process capability index
    (Multidisciplinary Digital Publishing Institute, 2019) Rao, Gadde Srinivasa; Albassam, Mohammed; Aslam, Muhammad
    This paper assesses the bootstrap confidence intervals of a newly proposed process capability index (PCI) forWeibull distribution, using the logarithm of the analyzed data. These methods can be applied when the quality of interest has non-symmetrical distribution. Bootstrap confidence intervals, which consist of standard bootstrap (SB), percentile bootstrap (PB), and bias-corrected percentile bootstrap (BCPB) confidence interval are constructed for the proposed method. A Monte Carlo simulation study is used to determine the efficiency of newly proposed index Cpkw over the existing method by addressing the coverage probabilities and average widths. The outcome shows that the BCPB confidence interval is recommended. The methodology of the proposed index has been explained by using the real data of breaking stress of carbon fibers.
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    Marshall–Olkin Power Lomax distribution for modeling of wind speed data
    (Elsevier Ltd, 2020) Ahsan ul Haq, Muhammad; Rao, Srinivasa G.; Albassam, Mohammed; Aslam, Mohammed
    Accurate collection of wind speed records is significant for numerous wind power applications. The present investigation aims to highlight the use of the Marshall–Olkin Power Lomax (MOPLx) distribution for wind speed data. We examine the actual wind speed records gathered from three stations Bahawalpur, Gwadar, and Haripur. The dataset is demonstrated by using MOPLx distribution and compare its modeling performance with renowned probability distributions, forexample, Weibull–Lomax, power Lomax, Weibull, power Lindley, Lindley, and Lomax. Findings indicate that MOPLx distribution gives the best fitting as per model evaluation criteria, Akaike information criterion (AIC), Bayesian information criterion (BIC), Kolmogorov Smirnov test (KS), coefficient of determination (R2)and root mean square error (RMSE). Overall, the results demonstrate the feasibility, precision, and effectiveness of the MOPLx distribution for portraying wind speed modeling. It is also observed that the MOPLx model is ideal in terms of the power density error (PDE) criterion.