Academic faculty of the university,University of Hormozgan,Iran
Majid Hashempour is an Assistant Professor in the Department of Statistics, Faculty of Basic Sciences, at Hormozgan University, Iran. With a deep expertise in statistical inference, he has contributed significantly to the development of statistical models, focusing primarily on cumulative residual entropy (extropy) and its applications in various fields. He completed his Ph.D. in Statistics at Ferdowsi University of Mashhad in 2016, following his M.Sc. in Mathematical Statistics from Shiraz University. Dr. Hashempour has a rich academic career, teaching at university level and collaborating with researchers worldwide. His scholarly work focuses on the creation of dynamic statistical models for analyzing inaccuracy, reliability, and survival data. He is widely published in leading statistical journals and continues to make notable contributions to statistical theory and applications.
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Education
Dr. Majid Hashempour holds a Bachelor’s degree in Statistics from Ferdowsi University of Mashhad (1997-2002), an M.Sc. in Mathematical Statistics from Shiraz University (2002-2004), and a Ph.D. in Statistics with a focus on Statistical Inference from Ferdowsi University of Mashhad (2012-2016). His doctoral thesis was titled “Dynamic Version of Weighted Cumulative Residual Extropy and Its Applications.” During his academic journey, he specialized in the study of extropy, a measure of uncertainty and inaccuracy, which he later applied to dynamic systems, survival analysis, and statistical modeling. His education laid the foundation for his expertise in theoretical and applied statistics, contributing significantly to his current role as an assistant professor and a researcher in statistical inference.
Experience
Dr. Majid Hashempour is currently serving as an Assistant Professor at Hormozgan University, where he has been teaching and conducting research since his appointment. He is involved in the Department of Statistics, Faculty of Basic Sciences, and has a reputation for his rigorous research in statistical inference, particularly in areas concerning extropy, residual analysis, and reliability modeling. Dr. Hashempour’s academic career has been marked by a commitment to educating the next generation of statisticians while pursuing significant scholarly research. He collaborates with a network of national and international researchers, contributing to numerous high-impact journal publications. Beyond teaching, Dr. Hashempour is active in guiding graduate students and conducting workshops in advanced statistical methods. His experience spans both academic and applied statistics, including the development of statistical tools for real-world applications like survival data analysis and risk assessment.
Research Focus
Dr. Hashempour’s research primarily revolves around the field of statistical inference, with a particular focus on extropy-based models and their dynamic versions. Extropy, a measure of inaccuracy and uncertainty, serves as a core concept in his work, with applications ranging from reliability analysis to survival modeling. He is particularly interested in dynamic versions of cumulative residual extropy and past inaccuracy measures, exploring their properties, applications, and estimation techniques. His work often involves advanced statistical methods for analyzing order statistics, failure-time data, and reliability systems. Additionally, he explores the development of new lifetime distributions, such as the two-parameter extensions of the half-logistic family, and investigates their theoretical and practical properties. Dr. Hashempour’s research also includes applications in various industries, such as aircraft maintenance data, where statistical models are applied to assess risks and optimize decision-making processes. His contributions continue to shape statistical theory and practice.
Publications
- Dynamic Version of Weighted Cumulative Residual Extropy and Its Applications 📊📈
- Dynamic Version of Past Inaccuracy Measure Under PRHR Model Based on Extropy 🔍💡
- Extropy-Based Dynamic Cumulative Residual Inaccuracy Measure: Properties and Applications 🧠🔢
- Extropy: Dynamic Cumulative Past and Residual Inaccuracy Measures with Applications 📉🧮
- On Weighted Version of Dynamic Cumulative Residual Inaccuracy Measure Based on Extropy 📊📝
- Modified Cumulative Extropies of Doubly Truncated Random Variables 🔢🔒
- A New Two-Parameter Extension of Half-Logistic Distribution: Properties, Applications and Different Methods of Estimations 📉🎲
- On the Dynamic Residual Measure of Inaccuracy Based on Extropy in Order Statistics 📚📊
- Extropy-Based Inaccuracy Measure in Order Statistics 🧮📈
- A New Lindley Extension: Estimation, Risk Assessment and Analysis Under Bimodal Right Skewed Precipitation Data 🌧️🔬
- Extropy: Characterizations and Dynamic Versions 📊🔍
- Residual Inaccuracy Extropy and Its Properties 📉🔎
- A New Measure of Inaccuracy for Record Statistics Based on Extropy 📑📐
- A Weighted Topp-Leone G Family of Distributions: Properties, Applications for Modelling Reliability Data and Different Methods of Estimation 📊⚙️
- Weighted Cumulative Past Extropy and Its Inference 🧮🔍
- On Interval Weighted Cumulative Residual and Past Extropies 📉🔢
- On Dynamic Cumulative Past Inaccuracy Measure Based on Extropy 🧠📊
- An Extended Type I Half-Logistic Family of Distributions: Properties, Applications and Different Methods of Estimations 📊🎲
- On Weighted Cumulative Residual Extropy: Characterization, Estimation and Testing 🧮📑
- A New Two-Parameter Lifetime Distribution with Flexible Hazard Rate Function: Properties, Applications and Different Methods of Estimations ⏳🔢
- Mixture Representations of the Extropy of Conditional Mixed Systems and Their Information Properties 🧠📚
- Dynamic Systems with Baseline Exponential Distribution Based on Sequential Order Statistics Under a Power Trend for Hazard Rates 📉⚡
- Statistical Inference on the Basis of Sequential Order Statistics Under a Linear Trend for Conditional Proportional Hazard Rates 🧑🏫📊
- Bayesian Inference on Multiply Sequential Order Statistics from Heterogeneous Exponential Populations with GLR Test for Homogeneity 🧑🏫📐
- Evidences in Lifetimes of Sequential R-out-of-N Systems and Optimal Sample Size Determination for Burr XII Populations 📈🧮