1. Hashemi, B.H., M. Khodabin and K. Maleknejad, 2017. Numerical method for solving linear stochastic Ito-Volterra integral equations driven by fractional Brownian motion using hat functions. Turk. J. Math., 41: 611-624.
2. Hashemi, B.H. and M. Khodabin, 2017. Series expansion of Wiener integrals via block pulse functions. J. New Res. Math., 3: 25-32.
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3. Hashemi, B., M. Khodabin and K. Maleknejad, 2017. Numerical solution based on hat functions for solving nonlinear stochastic Ito Volterra integral equations driven by fractional Brownian motion. Mediterranean J. Math., Vol. 14. 10.1007/s00009-016-0820-7.
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4. Fallahpour, M., M. Khodabin and K. Maleknejad, 2017. Theoretical error analysis and validation in numerical solution of two-dimensional linear stochastic Volterra-Fredholm integral equation by applying the block-pulse functions. Cogent Math., Vol. 4. 10.1080/23311835.2017.1296750.
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5. Asgari, M. and M. Khodabin, 2017. Computational method based on triangular operational matrices for solving nonlinear stochastic differential equations. Int. J. Nonlinear Anal. Applic., 8: 169-179.
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6. Shekarabi, F.H., M. Khodabin and K. Maleknejad, 2016. Application of operational matrices to numerical solution of stochastic SIR model. Arabian J. Math., 5: 77-86.
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7. Shekarabi, F.H. and M. Khodabin, 2016. Numerical solutions of stochastic Lotka-Volterra equations via operational matrices. J. Interpolation Approximation Sci. Comput., 2016: 37-42.
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8. Shekarabi, F.H. and M. Khodabin, 2016. Numerical solution of a model for stochastic polymer equation driven by space-time Brownian motion via homotopy perturbation method. Int. J. Applied Comput. Math., 2: 485-498.
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9. Rostami, M. and M. Khodabin, 2016. An optimal method based on rationalized Haar wavelet for numerical solution of stochastic Ito-Volterra integral equations. J. Operational Res. Applic., 6: 39-52.
10. Rahmani, N., M. Khodabin and E. Hashemizadeh, 2016. Numerical solution of stochastic SIR model by Bernstein polynomials. J. Interpolation Approximation Sci. Comput., 2016: 19-25.
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11. Khodabin, M., 2016. Some aspects in population growth rate. J. Interpolation Approximation Sci. Comput., 1: 14-18.
12. Khodabin, M., 2016. Entropy, uncertainty and related concepts in Brownian motion process. J. Fuzzy Set Valued Anal., 1: 19-30.
13. Khodabin, M. and M. Rostami, 2016. Numerical solution of m-dimensional stochastic it^o-volterra integral equations by stochastic operational matrix based on rationalized Haar wavelet. Adv. Differential Equations Control Proc., 17: 189-212.
14. Jami, P., M. Khodabin and E. Hashemizadeh, 2016. Numerical solution of stochastic SIR model via split-step forward Milstein method. J. Interpolation Approximation Sci. Comput., 2016: 38-45.
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15. Farahani, B., M. Khodabin and R. Ezzati, 2016. Numerical solution of stochastic nonlinear Volterra integral equations by a stochastic operational matrix based on Haar wavelets. Adv. Applic. Stat., 48: 317-336.
16. Fallahpour, M., M. Khodabin and K. Maleknejad, 2016. Approximation solution of two-dimensional linear stochastic Volterra-Fredholm integral equation via two-dimensional Block-pulse functions. Int. J. Ind. Math., 8: 423-430.
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17. Khodabin, M., K. Maleknejad and M. Fallahpour, 2015. Approximation solution of two-dimensional linear stochastic fredholm integral equation by applying the Haar wavelet. Int. J. Math. Modell. Comput., 5: 361-372.
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18. Khodabin, M. and M. Rostami, 2015. Mean square numerical solution of stochastic differential equations by fourth order Runge-Kutta method and its application in the electric circuits with noise. Adv. Difference Equations, Vol. 2015. 10.1186/s13662-015-0398-6.
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19. Talebi, J., K. Daneshfard and M. Khodabin, 2014. The impact of information technology on the performance of the human resource in the martyr foundation and veterans affairs of great Tehran. Universal J. Manage. Soc. Sci., 4: 1-11.
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20. Tahernia, N., M. Khodabin and N. Mirzaei, 2014. Non-Poisson probabilistic seismic hazard assessment. Arabian J. Geosci., 7: 3259-3269.
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21. Shekarabi, F.H., M. Khodabin and K. Maleknejad, 2014. The Petrov-Galerkin method for numerical solution of stochastic volterra integral equations. IAENG Int. J. Applied Math, 44: 1-7.
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22. Maleknejad, K., M. Khodabin and F.H. Shekarabi, 2014. Modified block pulse functions for numerical solution of stochastic Volterra integral equations. J. Applied Math., 10.1155/2014/469308.
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23. Khodabin, M., 2014. ADK divergence measure and testing exponentiality based on estimated ADK information. Int. J. Applied Math. Res., 3: 446-453.
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24. Khodabin, M. and V. Hosseinitoudeshki, 2014. Confidence interval in Kirsch equations. Int. J. Applied Math. Res., 3: 375-379.
25. Khodabin, M. and N. Kiaee, 2014. Stochastic dynamical theta-logistic population growth model. SOP Trans. Stat. Anal., 1: 1-15.
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26. Khodabin , M., K. Maleknejad and T. Damercheli, 2014. Approximate solution of the stochastic Volterra integral equations via expansion method. Int. J. Ind. Math., 6: 41-48.
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27. Ezzati, R., S. Salahshour, R.R. Yager and M. Khodabin, 2014. Fuzzy linear and nonlinear integral equations: Numerical methods. Abstr. Applied Anal., Vol. 2014. 10.1155/2014/147351.
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28. Ezzati, R., M. Khodabin and Z. Sadati, 2014. Numerical solution of backward stochastic differential equations driven by Brownian motion through block pulse functions. Indian J. Sci. Technol., 7: 271-275.
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29. Ezzati, R., M. Khodabin and Z. Sadati, 2014. Numerical implementation of stochastic operational matrix driven by a fractional Brownian motion for solving a stochastic differential equation. Abstract Applied Anal., 10.1155/2014/523163.
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30. Asgari, M., E. Hashemizadeh, M. Khodabin and K. Maleknejad, 2014. Numerical solution of nonlinear stochastic integral equation by stochastic operational matrix based on Bernstein polynomials. Bull. Math. Soc. Sci. Math., 57: 3-12.
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31. Khodabin, M., K. Maleknejad and M. Asgari, 2013. Numerical solution of a stochastic population growth model in a closed system. Adv. Difference Equations, 10.1186/1687-1847-2013-130.
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32. Khodabin, M., K. Maleknejad and F.H. Shekarabi, 2013. Numerical solution of stochastic Volterra integral equations via triangular functions. IAENG Int. J. Applied Math., 43: 1-9.
33. Khodabin, M., 2013. States recognition in random walk Markov chain via binary entropy. J. Interpolation Approximation Scient. Comput., .
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34. Khodabin, M., 2013. An application of trajectories ambiguity in two-state Markov chain. Int. J. Math. Model. Comput., 2: 221-229.
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35. Khodabin, M. and R. Ezzati, 2013. Entropy rate for Ehrenfest's urn models. Essays Math. Stat., 3: 135-149.
36. Khodabin, M. and M.A. Naeini, 2013. Confidence interval for number of population in dynamical stochastic exponential population growth models. Int. J. Applied Math. Res., 2: 403-407.
37. Ezzati, R., M. Khodabin and M. Salahaddin, 2013. A new approach for defuzzification of a fuzzy number and its application for ranking fuzzy numbers. J. Fuzzy Set Valued Anal., 10.5899/2013/jfsva-00156.
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38. Tahernia, N., M. Khodabin, N. Mirzaei and M. Eskandari, 2012. Statistical models of interoccurrence times of Iranian earthquakes on the basis of information criteria. Earth Syst. Sci., 121: 463-474.
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39. Maleknejad, K., M. Khodabin and M. Rostami, 2012. Numerical solution of stochastic Volterra integral equations by a stochastic operational matrix based on block pulse functions. Math. Comput. Model., 55: 791-800.
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40. Maleknejad, K., M. Khodabin and M. Rostami, 2012. A numerical method for solving m-dimensional stochastic Ito-Volterra integral equations by stochastic operational matrix. Comput. Math. Applic., 63: 133-143.
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41. Khodabin, M., K. Maleknejad, M. Rostami and M. Nouri, 2012. Numerical approach for solving stochastic Volterra-Fredholm integral equations by stochastic operational matrix. Comput. Math. Applic., 64: 1903-1913.
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42. Khodabin, M., K. Maleknejad, M. Rostami and M. Nouri, 2012. Interpolation solution in generalized stochastic exponential population growth model. Applied Math. Model., 36: 1023-1033.
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43. Tahernia, N., M. Khodabin and N. Mirzaei, 2011. Mixed model for interoccurrence times of earthquakes based on the expectation-maximization algorithm. Acta Geophys.,‎ 59: 872-890.
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44. Khodabin, M., K. Maleknejad, M. Rostami and M. Nouri, 2011. Numerical solution of stochastic differential equations by second order Runge-Kutta methods. Math. Comput. Modell., 53: 1910-1920.
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45. Khodabin, M., 2011. Some wonderful statistical properties of Pi-number decimal digits. J. Math. Sci.: Adv. Applic., 11: 69-77.
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46. Khodabin, M., 2011. Some properties of ADK entropy and ADK entropy rate. Proc. Comput. Sci., 3: 1170-11777.
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47. Khodabin, M., 2011. Asymptotic distribution of divergence measure with applications. J. Applied Math.,‎ 8: 41-53.
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48. Khodabin, M. and N. Kiaee, 2011. Stochastic dynamical logistic population growth model. J. Math. Sci. Adv. Applic., 11: 11-29.
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49. Khodabina, M. and A. Alireza, 2010. Some properties of generalized gamma distribution. Math. Sci., 4: 9-28.
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50. Khodabin, M., 2010. ADK entropy and ADK entropy rate in irreducible-aperiodic Markov chain and Gaussian processes. J. Iran. Stat. Soc.‎, 9: 115-126.
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51. Khodabin, M. and R.K. Matin, 2010. A statistical test for outlier identification in data envelopment analysis. Iran. J. Optimization, 4: 211-218.
52. Pasha, E., A. Beitollahi and M. Khodabin, 2007. Divergence measure and testing statistical hypothesis. Pak. J. Stat., 23: 205-220.
53. Khodabin, M., 2007. Some of nonlinear time series models and applications. Math. Sci. J. Islamic Azad Univ. Arak, 1: 67-95.
54. Pasha, E., M. Khodabin and G.R. Mohtashami Borzadran, 2006. Entropy in exponential families. J. Sci. Islamic Azad Univ., 16: 1-9.
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55. Pash, E., M. Khodabin and G.R.M. Borzadran, 2005. Hypothesis testing via shannon's entropy in exponential family and its application in comparison R populations. IUST Int. J. Eng. Sci., 16: 17-20.
56. Pash, E., M. Khodabin and G.R.M. Borzadran, 2004. Testing statistical hypothesis via Shannon's entropy in exponential families. Iran. Int. J. Sci., 5: 267-279.