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
RESEARCH INTERESTS
Financial Mathematics
Data Science
Actuarial Science
Sequential Estimation
Bayesian Inference
Distribution Theory
Novel randomized response method for mean estimation using exponential estimators Hamed Salemian, Eisa Mahmoudi, Javid Shabbir Communications in Statistics Theory and Methods, 2026 .In applied research, the objective of the study often involves topics of a sensitive nature. When the research objective has such characteristics, using direct questioning methods in sampling typically does not yield reliable results. This is because respondents may either refuse to answer or provide responses that may deviate significantly from the truth. To address this issue, random response techniques are employed. This method reduces non response bias and leads to more reliable estimates. In this article, we introduce a novel three-mode randomized response method and demonstrate its superiority through simulation studies. Furthermore, we develop exponential-type estimators using auxiliary information to improve the estimation of the mean of the sensitive variable. We show that under certain conditions, these estimators outperform the classical version. As a practical application, we refine the estimation of the average level of academic dishonesty among students at Shahid Chamran University of Ahvaz using the proposed exponential-type estimators.
Jackknife and Transformed Jackknife Empirical Likelihood Inferences for the Lifetime Performance Index with Missing Data Javad Estabraqi, Eisa Mahmoudi, Hossein Nadeb Mathematical Methods of Statistics, 2025 Abstract An important topic in manufacturing industries is assessing the lifetime performance using a taken sample. But many factors may be implied to encounter with missing data. We propose the jackknife empirical likelihood (JEL) and transformed jackknife empirical likelihood (TJEL) methods to construct confidence intervals for the lifetime performance index with missing data. After using hot deck imputation, we apply the JEL and TJEL. Simulation studies are utilized to evaluate the proposed methods and some competitors in terms of coverage probability and average lengths of confidence intervals. A real data set is used to illustrate the proposed JEL and TJEL and the competitor methods.
Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS) Fatemeh Mahmoodi, Aref Andishgar, Eisa Mahmoudi, Alireza Monsef, Sina Bazmi, Reza Tabrizi BMC Research Notes, 2024 OBJECTIVES: To enhance the identification of individuals at risk of developing clinically significant kidney stones. METHODS: In this study, data from the Fasa Adults Cohort Study were analyzed to explore factors linked to symptomatic and clinically significant kidney stone disease. After cleaning, 10,128 participants with 103 variables were studied. One outcome variable (presence of symptomatic kidney stones) and 102 predictor variables from surveys and tests were assessed. Five Machine learning (ML) algorithms (SVM, RF, KNN, GBM, XGB) were applied to examine kidney stone factors, with performance comparisons made. Data balancing was done using SMOTE, and metrics like accuracy, precision, sensitivity, specificity, F1 score, and AUC were evaluated for each algorithm. RESULTS: The XGB model outperformed others with AUC of 0.60, while RF, GBM, SVC, and KNN had AUC values of 0.58, 0.57, 0.54, and 0.52. RF, GBM, and XGB showed good accuracy at 0.81, 0.81, and 0.77. Top predictors for kidney stones were serum creatinine, salt intake, hospitalization history, sleep duration, and BUN levels. CONCLUSIONS: ML models show promise in evaluating an individual's risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.
Improving a novel quantitative randomized response method using auxiliary variable information Hamed Salemian, Eisa Mahmoudi, Osama Abdulaziz Alamri, Javid Shabbir Heliyon, 2024 In the majority of sample surveys, the variable of interest might have sensitive characteristics. Since we frequently get unrealistic responses from respondents during interviews as employing the direct method is not seen logical in these situations. To tackle this difficulty, the randomized response technique is good substitute for the direct method. The respondent is reassured that there would not be any issues will your response when using the randomized response technique as it protects privacy and secrets. In this paper, we propose a novel quantitative three-stage randomized response technique. Using simulation with R software, we prove that the results obtained from the proposed method are ideal. We suggest ratio and product type estimators by using the auxiliary information to enhance the efficiency of estimators. It is demonstrated that these estimators are superior for the conventional ones in special situations and in applied task, the use of auxiliary variable improves the estimation of the average cheating of Shahid Chamran university of Ahvaz students. The main objective is to increase the average sensitive reaction by utilizing the auxiliary variable.
Two-stage estimation of the combination of location and scale parameter of the exponential distribution under the constraint of bounded risk per unit cost index Eisa Mahmoudi, Zahra Nemati, Ashkan Khalifeh Sequential Analysis, 2023 We consider the problem of bounded risk point estimation for the linear combination of the form where and are the location and scale parameters of a exponential distribution and and are constant. We aim to estimate under the modified squared error loss function using the constraint that the risk per unit cost is bounded above with fixed preassigned, . The two-stage sequential sampling is proposed for estimating The performances of the proposed methodologies are investigated with the help of simulations. Finally, using an actual dataset, the procedure is clearly illustrated.
Copula-Based Reliability for Weighted-k-Out-of-n Systems Having Randomly Chosen Components of m Different Types Eisa Mahmoudi, Rahmat Sadat Meshkat, Hamzeh Torabi IEEE Transactions on Reliability, 2022 In this article, we consider a weighted-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-out-of-<inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> system having <inline-formula><tex-math notation="LaTeX">$m \\geq 2$</tex-math></inline-formula> type of components each with its own positive integer-valued weight, in which the random lifetimes of components are dependent. This system is supposed to work with performance level <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> if and only if the total weight of functioning components of all types is at least <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>. The structure of dependence of the system component lifetimes is modeled by the copula function. Also, it is assumed that the random numbers <inline-formula><tex-math notation="LaTeX">$N_i$</tex-math></inline-formula> of components are chosen from class <inline-formula><tex-math notation="LaTeX">$D_i$</tex-math></inline-formula> for type <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula>. The reliability of the system is obtained as a mixture of the reliability of weighted-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-out-of-<inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> systems consisting of <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> types of components with fixed number of them in terms of the probability mass function of the random vector <inline-formula><tex-math notation="LaTeX">$(N_1, \\ldots,N_{m-1})$</tex-math></inline-formula>. Then, a copula-based expression for component importance in each class is obtained, and illustrative examples are presented.
Modelling insurance losses using a new beta power transformed family of distributions Zubair Ahmad, Eisa Mahmoudi, Morad Alizadeh Communications in Statistics Simulation and Computation, 2022 Actuaries are often in search of new distributions suitable for modeling financial and insurance losses. In this work, we propose a new family of distributions, called a new beta power transformed family of distributions. A special sub-model of the proposed class, called a new beta power transformed Weibull, suitable for modeling heavy tailed data in the scenario of actuarial statistics and finance, is considered in detail. The proposed distribution possesses desirable properties relevant to actuarial sciences. Expressions for the actuarial quantities such as value at risk, tail value at risk, tailed variance and tailed variance premium are derived. A simulation study is conducted to evaluate the behavior of the proposed distribution in actuarial sciences. Some distributional properties with estimation of parameters using maximum likelihood method are also discussed. Finally, a practical application of the proposed model to insurance data is presented.
Increased prevalence 12308 A > g mutation in mitochondrial tRNA Leu(CUN) gene associated with earlier age of onset in friedreich ataxia Iranian Journal of Child Neurology, 2011
An admissible estimator of a lower-bounded scale parameter under squared-log error loss function Kybernetika, 2011
Jackknife empirical likelihood inference for the lifetime performance index J Estabraqi, E Mahmoudi, H Nadeb Communications in Statistics-Simulation and Computation 55 (5), 1450-1475 , 2026 2026 Citations: 1
A new class of median estimators using auxiliary information under PPS sampling: theoretical properties and empirical evaluation S Shah, E Mahmoudi, M Qureshi, H Iftikhar, PC Rodrigues, ... Computational Statistics 41 (3), 59 , 2026 2026
An Efficient Horvitz–Thompson-Type Estimator for Two Sensitive Means Using a Three-Stage Quantitative Randomized Response Under Complex Sampling H Salemian, E Mahmoudi, OA Alamri Axioms 15 (2), 108 , 2026 2026
Novel randomized response method for mean estimation using exponential estimators H Salemian, E Mahmoudi, J Shabbir Communications in Statistics-Theory and Methods, 1-19 , 2026 2026 Citations: 2
A Novel Family of CDF Estimators Under PPS Sampling: Computational, Theoretical, and Applied Perspectives S Shah, E Mahmoudi, H Iftikhar, PC Rodrigues, RI Gonzales Medina, ... Axioms 14 (11), 796 , 2025 2025 Citations: 4
Jackknife and Transformed Jackknife Empirical Likelihood Inferences for the Lifetime Performance Index with Missing Data J Estabraqi, E Mahmoudi, H Nadeb Mathematical Methods of Statistics 34 (2), 136-161 , 2025 2025
Improving a novel quantitative randomized response method using auxiliary variable information H Salemian, E Mahmoudi, OA Alamri, J Shabbir Heliyon 10 (22) , 2024 2024 Citations: 5
Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS) F Mahmoodi, A Andishgar, E Mahmoudi, A Monsef, S Bazmi, R Tabrizi BMC Research Notes 17 (1), 318 , 2024 2024 Citations: 10
Estimating the Means of Two Sensitive Variables with a New Quantitative Randomized Response Method H Salemian, E Mahmoudi, SMR Alavi Journal of Statistical Sciences 18 (1), 73-89 , 2024 2024
Estiⅿating the Ⅿeans of Two Sensitive Variabⅼes with a New Quanti− tative Ranⅾoⅿizeⅾ Response Ⅿethoⅾ H Salemian, E Mahmoudi, SMR Alavi Journal of Statistical Sciences 18 (1), 73-89 , 2024 2024
Two-stage estimation of the combination of location and scale parameter of the exponential distribution under the constraint of bounded risk per unit cost index E Mahmoudi, Z Nemati, A Khalifeh Sequential Analysis 42 (3), 211-227 , 2023 2023 Citations: 3
Modeling zero-inflated and zero-deflated count data time series using the INMA (1) process A Rostami, E Mahmoudi Journal of Statistical Modelling: Theory and Applications 4 (1), 45-58 , 2023 2023 Citations: 1
Bounded risk per unit cost index constraint for sequential estimation of the mean in a two-parameter exponential distribution E Mahmoudi, Z Nemati, A Khalifeh Sequential Analysis 41 (3), 285-309 , 2022 2022 Citations: 3
Modified Two-Stage Sampling Around the Mean of the First-Order Autoregressive Model E Mahmoudi, S Sajjadipanah, MS Zamani Journal of Statistical Sciences 16 (1), 127-148 , 2022 2022 Citations: 2
Modelling insurance losses using a new beta power transformed family of distributions Z Ahmad, E Mahmoudi, M Alizadeh Communications in Statistics-Simulation and Computation 51 (8), 4470-4491 , 2022 2022 Citations: 31
A new family of heavy tailed distributions with an application to the heavy tailed insurance loss data Z Ahmad, E Mahmoudi, S Dey Communications in Statistics-Simulation and Computation 51 (8), 4372-4395 , 2022 2022 Citations: 78
The Power Series Exponential Power Series Distributions with Applications to Failure Data Sets R Roozegar, S Nadarajah, E Mahmoudi Sankhya B 84 (1), 44-78 , 2022 2022
A class of claim distributions: properties, characterizations and applications to insurance claim data Z Ahmad, E Mahmoudi, G Hamedani Communications in Statistics-Theory and Methods 51 (7), 2183-2208 , 2022 2022 Citations: 47
Contributions towards new families of distributions: An investigation, further developments, characterizations and comparative study Z Ahmad, E Mahmoudi, R Roozegarz, GG Hamedani, NS Butt Pakistan Journal of Statistics and Operation Research, 99-120 , 2022 2022 Citations: 11
On Modeling Heavy Tailed Medical Care Insurance Data via a New Member of T-X Family Z Ahmad, E Mahmoudi, GG Hamedani, O Kharazmi Filomat 36 (6), 1971-1989 , 2022 2022 Citations: 17
MOST CITED SCHOLAR PUBLICATIONS
Generalized poisson–lindley distribution E Mahmoudi, H Zakerzadeh Communications in Statistics—theory and Methods 39 (10), 1785-1798 , 2010 2010 Citations: 180
Generalized exponential–power series distributions E Mahmoudi, AA Jafari Computational Statistics & Data Analysis 56 (12), 4047-4066 , 2012 2012 Citations: 136
The beta generalized Pareto distribution with application to lifetime data E Mahmoudi Mathematics and computers in Simulation 81 (11), 2414-2430 , 2011 2011 Citations: 133
Exponentiated Weibull–Poisson distribution: Model, properties and applications E Mahmoudi, A Sepahdar Mathematics and computers in simulation 92, 76-97 , 2013 2013 Citations: 108
New methods to define heavy-tailed distributions with applications to insurance data Z Ahmad, E Mahmoudi, GG Hamedani, O Kharazmi Journal of Taibah University for Science 14 (1), 359-382 , 2020 2020 Citations: 91
A new family of heavy tailed distributions with an application to the heavy tailed insurance loss data Z Ahmad, E Mahmoudi, S Dey Communications in Statistics-Simulation and Computation 51 (8), 4372-4395 , 2022 2022 Citations: 78
The Exponential T‐X Family of Distributions: Properties and an Application to Insurance Data Z Ahmad, E Mahmoudi, M Alizadeh, R Roozegar, AZ Afify Journal of Mathematics 2021 (1), 3058170 , 2021 2021 Citations: 60
A new two parameter lifetime distribution: model and properties H Zakerzadeh, E Mahmoudi arXiv preprint arXiv:1204.4248 , 2012 2012 Citations: 59
A New Class of Heavy‐Tailed Distributions: Modeling and Simulating Actuarial Measures J Zhao, Z Ahmad, E Mahmoudi, EH Hafez, MM Mohie El-Din Complexity 2021 (1), 5580228 , 2021 2021 Citations: 55
Modeling Vehicle Insurance Loss Data Using a New Member of T- X Family of Distributions Z Ahmad, E Mahmoudi, S Dey, SK Khosa Journal of Statistical Theory and Applications 19 (2), 133-147 , 2020 2020 Citations: 49
A class of claim distributions: properties, characterizations and applications to insurance claim data Z Ahmad, E Mahmoudi, G Hamedani Communications in Statistics-Theory and Methods 51 (7), 2183-2208 , 2022 2022 Citations: 47
The compound class of linear failure rate-power series distributions: Model, properties, and applications E Mahmoudi, AA Jafari Communications in Statistics-Simulation and Computation 46 (2), 1414-1440 , 2017 2017 Citations: 46
The Arcsine-X Family of Distributions with Applications to Financial Sciences. YL Tung, Z Ahmad, E Mahmoudi Comput. Syst. Sci. Eng. 39 (3), 351-363 , 2021 2021 Citations: 45
Beta-linear failure rate distribution and its applications AA Jafari, E Mahmoudi Journal of Iranian Statistical Science (JIRSS) 14 (1), 89-105 , 2015 2015 Citations: 36
A New flexible bathtub‐shaped modification of the Weibull model: properties and applications Q Liao, Z Ahmad, E Mahmoudi, GG Hamedani Mathematical Problems in Engineering 2020 (1), 3206257 , 2020 2020 Citations: 34
Joint reliability and weighted importance measures of a k-out-of-n system with random weights for components RS Meshkat, E Mahmoudi Journal of Computational and Applied Mathematics 326, 273-283 , 2017 2017 Citations: 33
Modelling insurance losses using a new beta power transformed family of distributions Z Ahmad, E Mahmoudi, M Alizadeh Communications in Statistics-Simulation and Computation 51 (8), 4470-4491 , 2022 2022 Citations: 31
A family of loss distributions with an application to the vehicle insurance loss data Z Ahmad, E Mahmoudi, GG Hamedani Pakistan Journal of Statistics and Operation Research , 2019 2019 Citations: 31
Exponentiated Weibull-geometric distribution and its applications E Mahmoudi, M Shiran arXiv preprint arXiv:1206.4008 , 2012 2012 Citations: 26
On Modeling the Earthquake Insurance Data via a New Member of the T‐ X Family Z Ahmad, E Mahmoudi, O Kharazmi Computational intelligence and neuroscience 2020 (1), 7631495 , 2020 2020 Citations: 24
