View Profile

Eisa Mahmoudi

Statistics · Faculty Member, Yazd University, Iran

https://researchid.co/emahmoudi
@yazd.ac.ir
56Scopus Publications
1639Google Scholar Citations
21Google Scholar h-index
35Google Scholar i10-index

Research Interests

Financial Mathematics Data Science Actuarial Science Sequential Estimation Bayesian Inference Distribution Theory

Biography

I am a Professor of Statistics and a faculty member for 16 years. Skilled in statistical techniques, data analysis, statistical modelling, statistical inference, survey methodology, actuarial science, financial mathematics and data science. Teaching various graduate and undergraduate courses in statistics and mathematics at the university level, and having experience in analyzing various data by statistical software, especially using R programming. Supervised/co-supervised 7 PhDs and 45 MSc students in various fields of statistics including, Mathematical Statistics, Reliability Analysis, Sequential Analysis, Financial Mathematics and Actuarial Science.

Education

SEP 2001 - JUN 2006 Ph.D. Department of Statistics, Shiraz University, Iran, Dissertation: Sequential Point Estimation in a Scale Family of Distributions SEP 1999 - AUG 2001 M.Sc. Department of Statistics, Shiraz University, Iran Dissertation: A Survey on Bayesian Convolution for Estimation SEP 1995 - JUNE 1999 B.Sc. Department of Statistics, Shiraz University, Iran

Recent Scopus Publications

  1. A new class of median estimators using auxiliary information under PPS sampling: theoretical properties and empirical evaluation
    Computational Statistics, 2026
  2. Novel randomized response method for mean estimation using exponential estimators
    Communications in Statistics Theory and Methods, 2026
  3. Jackknife empirical likelihood inference for the lifetime performance index
    Communications in Statistics Simulation and Computation, 2026
  4. Jackknife and Transformed Jackknife Empirical Likelihood Inferences for the Lifetime Performance Index with Missing Data
    Mathematical Methods of Statistics, 2025
  5. Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS)
    BMC Research Notes, 2024

Links