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Iman Mohammed Attia

Publications and source records attributed to Iman Mohammed Attia.

5 recordsLinked to original sources
arXiv2025

Median Based Unit Weibull Distribution (MBUW): Do the Higher Order Probability Weighted Moments (PWM) Add More Information over the Lower Order PWM in Parameter Estimation

In the present paper, Probability weighted moments (PWMs) method for parameter estimation of the median based unit weibull (MBUW) distribution is discussed. The most widely used first order PWMs is compared with the higher order PWMs for parameter estimation of (MBUW) distribution. Asymptotic distribution of this PWM estimator is derived. This comparison is illustrated using real data analysis.

stat.ME↗
arXiv2025

Comparative Study of the Median Based Unit Rayleigh and its Generalized Form the Generalized Odd Median Based Unit Rayleigh

In the present paper, the author discusses the Generalized Odd Median Base Unit Rayleigh (GOMBUR) in relation to the Median Based Unit Rayleigh (MBUR) to evaluate the additive value of the new shape parameter on the estimation process as regards validity indices, goodness of fit statistics, estimated variances of the estimated parameters and their standard errors. This evaluation is conducted on real datasets. Each dataset is analyzed by fitting different competitor distributions in addition to MBUR and GOMBUR distributions. The parameter estimation is achieved by applying Maximum likelihood estimator (MLE) using Nelder Mead optimizer.

stat.AP↗
arXiv2025

Median Based Unit Weibull (MBUW): a new unit distribution Properties

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.

stat.ME↗
arXiv2025

Bias-Corrected and Variance-Corrected MLE for the New Median Based Unit Weibull Distribution (MBUW)

As the maximum likelihood method is the most commonly used method for parameters estimation being unbiased, consistent, efficient, and asymptotically normal, MLE is used to fit the new distribution (MBUW). But in small to moderate sample size, this MLE estimator is biased unlike the MLE estimators obtained from large sample sizes. In this paper, the Bias-corrected approach for this distribution is discussed and applied to real data analysis. The MLE estimators of MBUW obtained from some optimization techniques like derivative free Nelder Mead algorithm suffers from significant high correlation that is reflected on high covariance between the parameters. Also this association between the parameters affects the variances which may be inflated enough to approach infinity hampering construction of confidence intervals for each parameter. This problem may arise with any optimization technique which necessitates remedies trying to fix it. The author also elaborates a variance correction approach heavily relaying on re-parameterizing the negative log likelihood.

stat.ME↗