morijamshidian

Contact Information

Office: MH 182A

Phone: 657-278-2398

Email: mori@fullerton.edu 

Mortaza (Mori) Jamshidian

Professor

Degrees

PhD, Applied Mathematics, University of California, Los Angeles

MA, Applied Mathematics, University of California, Los Angeles

BA, Mathematics, California State Polytechnic University, Pomona

Research Areas

Computational Statistics; Statistics Education; Analysis of Incomplete Data; Computational Psychometrics; Biostatistics

Courses Regularly Taught

Graduate level: Statistical Computing, Statistical Consulting, Probability and Statistical Inference
Undergraduate level: Introductory Statistics, Statistics Applied to Natural Sciences, Intermediate Data Analysis, Mathematical Probability, Numerical Analysis

Publications

  • Jamshidian, M. & Jamshidian, P. (2024). Teaching statistical inference through a conceptual lens: A spin on existing methods with examples. Journal of Statistics and Data Science Education, 32 (1), 54-72. https://doi.org/10.1080/26939169.2023.2190011
  • Yuan, K.H., Jamshidian, M., & Kano, Y. (2018). MissMech: Missing data mechanism and homogeneity of means and variance-covariances. Psychometrika, 83 (2), 425-442.
  • Jamshidian, M., Jalal, S., & Jansen, C. (2014). MissMech: An R package for testing homoscedasticity, multivariate normality, and missing completely at random. Journal of Statistical Software, 56 (6), 1-31.
  • Jamshidian, M. & Yuan, K-H (2014). Examining missing data mechanisms via homogeneity of parameters, homogeneity of distributions, and multivariate normality.WIREs Computational Statistics, 6, 56-73.
  • Jamshidian, M. & Yuan, K-H (2013). Data-driven sensitivity analysis to detect missing data mechanism with applications to structural equation modeling. Journal of Statistical Computation and simulation, 83, 1344-1362.
  • Jamshidian, M. & Jalal, S. (2010). Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika, 75 (4), 649-674.
  • Jamshidian, M., Liu, W., & Bretz, F. (2010). Simultaneous confidence bands for all contrasts of three or more simple linear regression models over an interval. Computational Statistics and Data Analysis, 54, 1475-1483.
  • Jamshidian, M. & Mata, M. (2008). Post modeling sensitivity analysis to detect the effect of missing data mechanisms. Multivariate Behavioral Research, 43, 432-452.
  • Jamshidian, M., Jennrich, R. I., & Liu, W.(2007). A study of partial F tests for multiple linear regression models. Computational Statistics and Data Analysis, 51, 6269-6284.
  • Jamshidian, M. & Khatoonabadi, M. (2007).Using asymptotic results to obtain a confidence interval for the population median. International Journal of Mathematical Education in Science and Technology, 38, 805-817.
  • Jamshidian, M. & Schott, J. (2007). Testing equality of covariance matrices when data are incomplete. Computational Statistics and Data Analysis, 51, 4227-4239.
  • Liu, W., Jamshidian, M., Zhang, Y., Bertz, F., & Han, X. (2007). Pooling batches in drug stability study by using constant-width simultaneous confidence bands. Statistics in Medicine, 26 (14), 2759-2771.
  • Liu, W., Jamshidian, M., Zhang, Y., Bertz, F., & Han, X. (2007). Some new methods for the comparison of two linear regression models. Journal of Statistical Planning and Inference, 137, 57-67.
  • Jamshidian, M. & Mata, M. (2007). Advances in analysis of mean and covariance structures when data are incomplete. In Handbook of Latent Variable and Related Models, (S. Y. Lee, Ed.), Chapter 2,  21-44, Amsterdam: Elsevier.
  • Miller, H.L., Stratton, L.R., Hollerbach, M.A., & Jamshidian, M. (2006).The case for a kinetic energy criterion in control valves Part 3. Proceedings of the Ninth NRC/ASME Symposium on Valves, Pumps and InserviceTesting, NUREG/CP-0152, 6, 1B:1-1N:9.
  • Jamshidian, M., Liu, W., Zhang, Y., & Jamshidian, F. (2005). SimReg: A software including some new developments in multiple comparison and simultaneous confidence bands for linear regression models. Journal of Statistical Software, 12(2), 1-23.
  • Liu, W., Jamshidian, M., Zhang, Y., & Bertz, F. (2005). Constant width simultaneous confidence bands in multiple linear regression with predictor variables constrained in intervals. Journal of Statistical Computation and Simulation, 75(6), 425-436.
  • Liu, W., Jamshidian, M., Zhang, Y., & Donnelly, J. (2005). Simulation-based simultaneous confidence bands in multiple linear regression with predictor variables constrained in intervals. Journal of Computational and Graphical Statistics, 14(2), 459-484.
  • Jamshidian, M. (2004). On algorithms for restricted maximum likelihood estimation. Computational Statistics and Data Analysis, 45, 137-157.
  • Jamshidian, M. (2004). Strategies for analysis of missing data. In Handbook of Data Analysis, (M. Hardy, and A. Bryman Eds.). Sage Publication, 113-130.
  • Kantardjieff, K. A., Jamshidian, M., & Rupp, B. (2004). Distributions of pI versus pH provide prior information for the design of crystallization screening experiments: Response to comment on protein isoelectric point as a predictor for increased crystallization screening efficiency. Bioinformatics, 20, 2171-2174.
  • Liu, W., Jamshidian, M., & Zhang, Y. (2004). Multiple comparison of several linear regression lines.  Journal of the American Statistical Association, 99, 395-403.
  • Zhang, Y. & Jamshidian, M. (2004). On algorithms for NPMLE of the failure function with censored data. Journal of Computational and Graphical Statistics, 13, 123-140.
  • Zhang, Y. & Jamshidian, M. (2003). The gamma-frailty Poisson model for the nonparametric estimation of panel count data. Biometrics, 59, 1099-1106.
  • Jamshidian, M. (2001). A note on parameter and standard error estimation in adaptive robust regression. Journal of Statistical Computation and Simulation, 71, 11-28.
  • Jamshidian, M. & Jennrich, R. I. (2000). Standard errors for EM estimation. Journal of the Royal Statistical Society, Ser. B., 62, 257-270.
  • Jamshidian, M. & Bentler P. M. (2000). Improved standard errors of standardized parameters in covariance structure models: Implications for construct explication. In Problems and Solutions in Human Assessment: Honoring Douglas N. Jackson at Seventy, (Eds., R. D. Griffin and E. Holmes). Kluwer Academic, 73--94.
  • Jamshidian, M. (1999). Adaptive robust regression by using a nonlinear regression program. Journal of Statistical Software, 4, 1--25.
  • Jamshidian, M. & Bentler, P.M. (1999). Using complete data routines for ML estimation of mean and covariance structures with missing data. Journal of Educational and Behavioral Statistics, 24(1), 21-41.
  • Jamshidian, M. & Bentler, P. M. (1998). A quasi-Newton method for minimum trace factor analysis. Journal of Statistical Computation and Simulation, 62, 73-89.
  • Jamshidian, M. (1997). t-Distribution Modeling Using the Available Statistical Software. Computational Statistics and Data Analysis, 25, 181--206.
  • Jamshidian, M. (1997). An EM algorithm for ML factor analysis with missing data. In Latent Variable Modeling and Applications to Causality,(Ed. Berkane, M.). Springer Verlag, 247--258. 
  • Jamshidian, M. & Jennrich, R. I. (1997). Acceleration of the EM algorithm by using quasi-Newton methods. Journal of the Royal Statistical Society, Ser. B, 59, 569--587.
  • Bentler, P. M. & Jamshidian, M. (1994). Gramian matrices in covariance structure models. Applied Psychological Measurement, 18, 79--94.
  • Jamshidian, M. & Jennrich, R. I. (1994). Applications of conjugate gradient methods in confirmatory factor analysis. Computational Statistics and Data Analysis, 17, 247--263.
  • Jamshidian, M. & Bentler, P. M. (1993). A modified Newton method for constrained estimation in covariance structure analysis. Computational Statistics and Data Analysis, 15, 133--146.
  • Jamshidian, M. & Jennrich, R. I. (1993). Conjugate gradient acceleration of the EM algorithm. Journal of the American Statistical Association, 88, 221--228.
  • Jamshidian, M. & Jennrich, R. I. (1988). Nonorthogonal analysis of variance using gradient methods. Journal of the American Statistical Association, 83, 483--489.
  • Jamshidian, M. (1999). Constrained Maximum Likelihood Estimation by EM-Type Methods. ASA Proceedings of the Statistical Computing Section, 65-69.
  • Jamshidian, M. (1997). Adaptive Robust Regression by Using a Nonlinear Regression Program. ASA Proceedings of the Statistical Computing Section, 188-193.
  • Jamshidian, M. & Jennrich, R. I. (1997). Standard Errors for EM Estimation. Computing Science and Statistics, 29, 463-470, (E. Wegmanand S. Azen, eds.). Interface foundation of North America, Inc.: Fairfax, VAUSA.
  • Jamshidian, M. (1992). Graphical data analysis in linear regression. Proceedings of the First Iranian Statistics Conference: Invited Papers, 51--122.
  • Jamshidian, M. & Bentler, P. M. (1991). A direct numerical method for minimum trace factor analysis. UCLA Statistics Series, 89.
  • Jamshidian, M. (1989). Solving overparameterized problems using BMDP- 3R, application of Gauss-Newton and Marquardt methods. BMDP Communications, 21, 2--4.