Scholars

Professor

Positions:

Professor, Statistics, College of Arts and Sciences

overview

Time Series Analysis and Prediction Theory, Multivariate Statistics and Longitudinal Data Analysis, High-Dimensional Data Analysis, Financial Data and Stochastic Volatility Models.

education and training

Ph.D. in Statistics, Michigan State University - (East Lansing, Michigan, United States) 1980
M.S. in Statistics, Michigan State University - (East Lansing, Michigan, United States) 1977

selected publications

Academic Articles81

Kundu, A., & Pourahmadi, M. (2023). MLE of Jointly Constrained Mean-Covariance of Multivariate Normal Distributions. 85(1), 1-32.
Kundu, A., & Pourahmadi, M. (2023). Bayesian estimation of constrained mean-covariance of normal distributions. Statistics and Probability Letters. 194, 109745-109745.
Ghosh, R. P., Maity, A. K., Pourahmadi, M., & Mallick, B. K. (2023). Adaptive Bayesian variable clustering via structural learning of breast cancer data.. Genetic Epidemiology. 47(1), 95-104.
Dallakyan, A., & Pourahmadi, M. (2023). Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Replicated Time Series. Journal of Computational And Graphical Statistics (JCGS). 32(1), 157-170.
Dallakyan, A., Kim, R., & Pourahmadi, M. (2022). Time series graphical lasso and sparse VAR estimation. Computational Statistics and Data Analysis. 176, 107557-107557.

Books1

Pourahmadi, M. (2013). High-Dimensional Covariance Estimation With High-Dimensional Data. JOhn Wiley & Sons.

Chapters2

Kohli, P., & Pourahmadi, M. (2012). Nonparametric estimation of the innovation variance and judging the fit of ARMA models. Economic Time Series: Modeling and Seasonality. 459-476.
Pourahmadi, M., & Wu, W. B. (2008). Inference for Stationary Processes Using Banded Covariance Matrices. Dabo-Niang, S., & Ferraty, F. (Eds.), Functional and Operatorial Statistics. 255-261. Springer Nature.