| Fuzzy semi-parametric partially linear model with fuzzy inputs and fuzzy outputs G Hesamian, MG Akbari, M Asadollahi Expert Systems with Applications 71, 230-239, 2017 | 41 | 2017 |
| A robust varying coefficient approach to fuzzy multiple regression model G Hesamian, MG Akbari Journal of Computational and Applied Mathematics 371, 112704, 2020 | 34 | 2020 |
| Bayesian analysis for the two-parameter Pareto distribution based on record values and times M Doostparast, MG Akbari, N Balakrishna Journal of Statistical Computation and Simulation 81 (11), 1393-1403, 2011 | 33 | 2011 |
| Ridge estimation in semi-parametric regression models under the stochastic restriction and correlated elliptically contoured errors M Roozbeh, G Hesamian, MG Akbari Journal of Computational and Applied Mathematics 378, 112940, 2020 | 32 | 2020 |
| Quality control process based on fuzzy random variables G Hesamian, MG Akbari, R Yaghoobpoor IEEE Transactions on Fuzzy Systems 27 (4), 671-685, 2018 | 31 | 2018 |
| Bootstrap testing fuzzy hypotheses and observations on fuzzy statistic MG Akbari, A Rezaei Expert Systems with Applications 37 (8), 5782-5787, 2010 | 31 | 2010 |
| A partial-robust-ridge-based regression model with fuzzy predictors-responses MG Akbari, G Hesamian Journal of Computational and Applied Mathematics 351, 290-301, 2019 | 30 | 2019 |
| Bootstrap statistical inference for the variance based on fuzzy data MG Akbari, A Rezaei Austrian Journal of Statistics 38 (2), 121–130-121–130, 2009 | 29 | 2009 |
| A robust multiple regression model based on fuzzy random variables G Hesamian, MG Akbari Journal of Computational and Applied Mathematics 388, 113270, 2021 | 28 | 2021 |
| A fuzzy additive regression model with exact predictors and fuzzy responses G Hesamian, MG Akbari Applied Soft Computing 95, 106507, 2020 | 28 | 2020 |
| Modeling autoregressive fuzzy time series data based on semi-parametric methods R Zarei, MG Akbari, J Chachi Soft Computing 24 (10), 7295-7304, 2020 | 28 | 2020 |
| Elastic net oriented to fuzzy semiparametric regression model with fuzzy explanatory variables and fuzzy responses MG Akbari, G Hesamian IEEE Transactions on Fuzzy Systems 27 (12), 2433-2442, 2019 | 28 | 2019 |
| Statistical inference about the variance of fuzzy random variables MGH Akbari, AH Rezaei, Y Waghei Sankhyā: The Indian Journal of Statistics, Series B (2008-), 206-221, 2009 | 28 | 2009 |
| Semi-parametric partially logistic regression model with exact inputs and intuitionistic fuzzy outputs G Hesamian, MG Akbari Applied Soft Computing 58, 517-526, 2017 | 27 | 2017 |
| A robust least squares fuzzy regression model based on kernel function AH Khammar, M Arefi, MG Akbari Iranian Journal of Fuzzy Systems 17 (4), 105-119, 2020 | 25 | 2020 |
| Linear model with exact inputs and interval-valued fuzzy outputs MG Akbari, G Hesamian IEEE Transactions on Fuzzy Systems 26 (2), 518-530, 2017 | 25 | 2017 |
| A general approach to fuzzy regression models based on different loss functions AH Khammar, M Arefi, MG Akbari Soft Computing 25 (2), 835-849, 2021 | 23 | 2021 |
| A robust support vector regression with exact predictors and fuzzy responses M Asadolahi, MG Akbari, G Hesamian, M Arefi International Journal of Approximate Reasoning 132, 206-225, 2021 | 22 | 2021 |
| Fuzzy Lasso regression model with exact explanatory variables and fuzzy responses G Hesamian, MG Akbari International Journal of Approximate Reasoning 115, 290-300, 2019 | 22 | 2019 |
| Fuzzy quantile linear regression model adopted with a semi-parametric technique based on fuzzy predictors and fuzzy responses G Hesamian, MG Akbari Expert systems with applications 118, 585-597, 2019 | 21 | 2019 |
Gholamreza HesamianAssociate Professor of StatisticsEmail verificata su pnu.ac.ir