Mustafa

@department of statistics, university of delhi

Research Scholar, Department of Statistics, University of Delhi
Department of Statistics, University of Delhi

Mustafa


            Download Compact PDF  
https://researchid.co/immustafa

RESEARCH, TEACHING, or OTHER INTERESTS

Statistics and Probability, Modeling and Simulation, Safety, Risk, Reliability and Quality, Applied Mathematics
2

Scopus Publications

Scopus Publications

  • A NEW WEIGHTED WEIBULL-FRÉCHET MIXTURE (WWFM) DISTRIBUTION: PROPERTIES, ESTIMATION AND APPLICATIONS IN RELIABILITY ANALYSIS
    Pushkarna, Mustafa Raza
    Far East Journal of Theoretical Statistics, 2026
    In this paper, we introduce the Weighted Weibull-Fréchet Mixture (WWFM) distribution – a novel statistical model that flexibly combines the Weibull and Fréchet distributions using a weighting constant, and integrates it with a Non-Homogeneous Poisson Process (NHPP), and derives the key statistical properties of the distribution, including its density and distribution functions, reliability measures, hazard functions, entropy measures, and order statistics. Parameters are estimated using the maximum likelihood estimation (MLE) method. The performance of the proposed model is assessed using multiple real-world datasets and compared against established models through various goodness-of-fit criteria such as AIC, BIC, AICc, KS, Anderson-Darling, and Cramér-von Mises statistics. Visual tools – including KS plots, box plots, violin plots, and density plots – further support the practical utility of the model. Additionally, a simulation study demonstrates the consistency and efficiency of the MLE estimators. Overall, the WWFM distribution offers a flexible and robust framework for modeling lifetime and reliability data.
  • Length Biased Weighted Ishita Distribution and Its Applications on Real Life Data Sets
    Narinder Pushkarna, Mustafa
    Journal of Reliability and Statistical Studies, 2025
    In this paper, we introduce a new extension within the realm of statistical distributions, presenting the “length-biased Ishita distribution.” This distribution stands out as part of the esteemed category of weighted distributions, particularly the length-biased variation. Through meticulous analysis, we explore the mathematical and statistical properties of this novel distribution and reveal its distinct characteristics. Using the robust methodology of maximum likelihood estimation, we accurately estimate the model parameters, enhancing our understanding of its behavior. To demonstrate the practical utility and advantages of the length-biased Ishita distribution, we apply it to a real-world temporal dataset. This empirical analysis highlights its superior performance and adaptability, offering valuable insights into its potential applications across various domains.