TY  - EJOU
AU  - Obulezi, Okechukwu J. 
AU  - Semary, Hatem E. 
AU  - Nadir, Sadia 
AU  - Igbokwe, Chinyere P. 
AU  - Orji, Gabriel O. 
AU  - Al-Moisheer, A. S. 
AU  - Elgarhy, Mohammed 

TI  - Type-I Heavy-Tailed Burr XII Distribution with Applications to Quality Control, Skewed Reliability Engineering Systems and Lifetime Data
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 3
SN  - 1526-1506

AB  - This study introduces the type-I heavy-tailed Burr XII (TIHTBXII) distribution, a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness, heavy tails, and diverse hazard behaviors. We meticulously develop the TIHTBXII’s mathematical foundations, including its probability density function (PDF), cumulative distribution function (CDF), and essential statistical properties, crucial for theoretical understanding and practical application. A comprehensive Monte Carlo simulation evaluates four parameter estimation methods: maximum likelihood (MLE), maximum product spacing (MPS), least squares (LS), and weighted least squares (WLS). The simulation results consistently show that as sample sizes increase, the Bias and RMSE of all estimators decrease, with WLS and LS often demonstrating superior and more stable performance. Beyond theoretical development, we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan (GASP) for truncated life tests. This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number (ASN) while effectively managing consumer and producer risks. Empirical validation using real-world datasets, including “Active Repair Duration,” “Groundwater Contaminant Measurements,” and “Dominica COVID-19 Mortality,” further demonstrates the TIHTBXII’s superior fit compared to existing models. Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment, leading to more informed and robust decision-making.
KW  - Acceptance sampling; heavy-tailed models; parameter estimation; reliability engineering

DO  - 10.32604/cmes.2025.069553