Lifetime data analysis stands at the forefront of applied sciences, influencing fields as diverse as engineering, medicine, and finance. Traditional models like the exponential and Lindley distributions have their merits but often fall short in capturing the intricacies of real-world phenomena. Enter the Suja distribution, a novel model that promises a more adaptable approach to analyzing lifetime data. Its introduction marks a significant stride towards more accurately understanding the dynamics of failure and survival, offering a tool with enhanced flexibility and reliability for analyzing such critical data.

In a landmark study published in the Alexandria Engineering Journal, Professor Hanaa Abu-Zinadah and Tamadur Alsumairi from the University of Jeddah introduce a groundbreaking statistical model known as the Suja distribution. This innovative one-parameter distribution offers a novel approach to modeling lifetime data, crucial for a wide array of applications from engineering to finance. Their exploration of various estimation methods goes beyond the traditional maximum likelihood estimation, significantly enhancing the precision and reliability of statistical inference.

The initiative led by Professor Abu-Zinadah and Alsumairi to develop a more adaptable approach to modeling lifetime data marks a pivotal moment in the study of lifespan distributions. “In addition, it’s suggested and investigated a new one-parameter distribution named ‘Suja distribution’ for modeling lifetime data. The estimate of its parameter has been explored using maximum likelihood estimation and the method of moments. In this exploration, we are adopting a flexible one-parameter distribution for modeling lifetime data in terms of its hazard failure rate (HFR) shapes and reliability than these lifetime distributions called Suja distribution (SD),” explained Alsumairi.

The adaptability of the Suja distribution in accurately modeling the failure times of products, vital for assessing product quality and reliability, is at the core of their exploration. By conducting a comprehensive Monte Carlo simulation study, Professor Abu-Zinadah and Alsumairi were able to assess and compare the performance of different estimators for the Suja parameter, proving its superior adaptability and reliability across various real data sets.

“We aim to develop estimation of SD with different classical methods. Also, we conduct the goodness of fit tests for three real,” Professor Abu-Zinadah notes, emphasizing the breadth of their analysis and the comprehensive approach taken to validate the Suja distribution across various real-world datasets.

The exploration included methodologies such as least squares, weighted least squares, and estimators based on percentiles, demonstrating the Suja distribution’s flexibility across different scenarios. “It is relatively normal to estimate the unknown parameters when the data is derived from a distribution function with a closed form by fitting a straight line to the theoretical points acquired from the distribution function and the sample percentile points. This method has been used to estimate the parameters of many distributions.” explains Alsumairi, illustrating the practicality of their approach.

This comprehensive exploration opens new pathways in statistical modeling, promising significant progress in reliability analysis and other fields. The Suja distribution, with its robustness and adaptability, stands as a pioneering innovation, guiding Professor Abu-Zinadah and Alsumairi towards more precise and meaningful analyses of lifetime data. A detailed look at maximum likelihood estimation (MLE), alongside the probability density functions (PDFs) and hazard failure rates (HFRs) of the Suja distribution, complements their work. This is illuminated by a Monte Carlo simulation study that evaluates the performance of various estimators, showcasing the distribution’s reliability and adaptability without delving into overly technical jargon.


Hanaa Abu-Zinadah, Tamadur Alsumairi, ‘The estimations for parameter of Suja distribution with application’, Alexandria Engineering Journal, 2024. DOI:


Prof. Hanaa Abu-Zinadah

Hanaa Abu-Zinadah is Professor of Mathematical Statistics in Mathematics and Statistics Department, College of Science, University of Jeddah, Jeddah, Kingdom of Saudi Arabia. She was born in Jeddah, Kingdom of Saudi Arabia April 1976.

She got her bachelor’s degree of Mathematic (1996), master’s degree (2001) and Ph.D. degree (2006) of Mathematical Statistics from Mathematic Department, Scientific Section, Girls College of Education in Jeddah, Kingdom of Saudi Arabia.

She became the Head of Statistics Department, Faculty of Science – AL Faisaliah, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia from 2010 – 2019. She became full Professor of Mathematical Statistics since 2020.

She researches spans various domains within Statistics, including Distribution Theory, Statistical Inferences, Order Statistics, and Simulations Studies. Her expertise extends to various programming languages and statistical tools, enabling her to delve into complex analyses and statistical quality control.

Her working significantly impacts the realm of statistical sciences and interdisciplinary studies. Her research output, including papers on various mathematical models and their practical implications, demonstrates a fusion of theoretical rigor with real-world relevance.

Her continued dedication to statistical research and mentorship in guiding postgraduate students in their theses reflects a commitment to shaping the future of statistical sciences. Her contribution will undoubtedly inspire further advancements and innovations in statistical methodologies and their applications.


Tamadur Alsumairi is a statistical researcher and data analyst with a master’s degree in Statistics (2024) from the Faculty of Science, Department of Mathematics and Statistics at the University of Jeddah, Jeddah, Saudi Arabia. She also holds a bachelor’s degree in Statistics (2016) from the Faculty of Science, Department of Statistics at King Abdulaziz University, Jeddah, Saudi Arabia.

With a strong passion for numbers and data analysis, She has dedicated her academic and career to the field of statistics. Her educational background has equipped her with a deep understanding of statistical methodologies, mathematical modeling, and data interpretation.

During her studies, She gained hands-on experience in various statistical techniques, such as hypothesis testing, regression analysis, time series analysis, and multivariate analysis. She also acquired proficiency in programming languages commonly used in statistical analysis, such as R and analyze data using Excel and SPSS. Additionally, she used the Mathematica program for estimating parameters, conducting numerical simulations, and apply real data in scientific research.

Her academic journey has provided her with a solid foundation in statistical research and analysis, enabling her to effectively collect, organize, and analyze complex datasets. She is skilled in data visualization and can effectively communicate statistical findings and insights. She is detail-oriented, analytical, and possess strong problem-solving skills.

She is excited about the opportunity to apply her statistical knowledge and analytical skills to contribute to organizations in need of data-driven insights. She is constantly seeking to expand her knowledge and stay updated on the latest advancements in statistical methodologies and data analysis techniques. Email: