Shoja'eddin Chenouri

Professor / Associate Chair, Graduate Studies

Shoja'eddin Chenouri
Contact Information:
Shoja Chenouri

Research interests

Professor Chenouri's research interests include:

  • Data depth, 
  • Multivariate nonparametric and robust methods for complete, censored and incomplete data, 
  • Multivariate quality control, 
  • Geometry of high-dimensional data, 
  • Dimensionality reduction, 

  • Classification and regression with missing data, 
  • Inference on random graph processes,
  • Social and epidemic networks,
  • Statistical analysis of spike train data, 
  • Computational statistics,
  • Environmental and water quality research, and
  • Transportation research.

Selected publications

  • Ramsay, K., Chenouri, S. (2021). Differentially private depth functions and their associated medians. arXiv preprint arXiv:2101.02800

  • Ebadi, M., Chenouri, S., Lin, D., Steiner, S. (2021). Statistical Monitoring of Covariance Matrix in Multivariate Processes: A Literature Review. Journal of Quality Technology. To appear.

  • Hemati, S.,  Mehdizavareh, MH,  Chenouri, S. and Tizhoosh MH (2020). A non-alternating graph hashing algorithm for large scale image search arXiv preprint arXiv:2012.13138.

  • Ramsay, K., Chenouri, S. (2020). Robust, multiple change-point detection for covariance matrices using data depth arXiv preprint arXiv:2011.09558.

  • Mehrizi, RV, Chenouri S. (2020). Detection of Change Points in Piecewise Polynomial Signals Using Trend Filtering arXiv preprint arXiv:2009.08573.

  • Chenouri, S., Mozaffari, A. and Rice G. (2020). Robust multivariate change point analysis based on data depth Canadian Journal of Statistics 48 (3), 417-446

  • Liang, J., Chenouri, S. and Small, C.G. (2020). A new method for performance analysis in nonlinear dimensionality reduction. Statistical Analysis and Data Mining: The ASA Data Science Journal. 13, 1, 98-108.

  • Mostafaiy, B., FaridRohani, MR., Chenouri, S. (2019). Optimal estimation in functional linear regression for sparse noise‐contaminated data Canadian Journal of Statistics 47 (4), 524-559

  • Mozaffari*, A., Chenouri, S., Qin, Y. and Khajepour, A. (2019). Learning-based vehicle suspension controller design: A review of the state-of-the-art and future research potentials. eTransportation 2, 100024

  • Rajabi, M., Faridrohani, MR. and Chenouri, S. (2017). Phase I monitoring with nonparametric mixed effect models.  Quality and Reliability Engineering International, 33(8): 1929--1941.
  • Mozaffari, A., Scott, A., Azad, N. L. and Chenouri, S. (2017). A hierarchical selective ensemble randomized neural network hybridized with heuristic feature selection for estimation of sea-ice thickness. Applied Intelligence,  46(1): 16--33.
  • Gohari, M.,  Ramezani-Tehrani, F., Chenouri, S., Solaymani-Dodaran, M., and Azizi, F. (2016). Individualized predictions of time to menopause using multiple measurements of antimullerian hormone. Menopause - The Journal of The North American Menopause Society. 23(8): 839-845 
  • Ramezan, R. Marriott, P. and Chenouri, S. (2016). The Skellam process with resetting: a neural spike train model.  Statistics in Medicine, 35(30): 5717-5729.
  • Shoari, N., Dube, J.S. and Chenouri, S.(2016). On the use of the substitution method in left-censored environmental data. Human and Ecological Risk Assessment: An International Journal. 22(2): 435-446.
  • Chenouri, S., Kobelevskiy, P. and Small, C.G. (2015). Spanifold: Spanning tree flattening onto lower dimension. Stat, 4, 15--31.
  • Chenouri, S., Liang, J. and Small, C.G. (2015). Robust dimension reduction. Wiley Interdisciplinary Reviews: Computational Statistics. 7, 63–69.
  • Ramezan, R., Marriott, P.K. and Chenouri, S. (2014). Multiscale analysis of neural spike trains. Statistics in Medicine. 33, 238--256.
  • Chenouri, S. and Small, C. G. (2012). A Multivariate Nonparametric Multi-Sample Test based on Data Depth. Electronic Journal of Statistics, 6, 760-782.
  • Hosseinkashi, Y. Chenouri, S., Small, C. G., and Deardon, R. (2012). A Stochastic Graph Process for Epidemic Modelling. Canadian Journal of Statistics, 40, 55-67.
  • Chenouri, S. and Small, C. G. and Farrar, T. J.  (2011). Data Depth-Based Nonparamertic Scale Tests. Canadian Journal of Statistics, 39, 356-369.
  • Chenouri, S. and Mulaya Variath, A. (2011). A Comparative Study of Phase II Robust Control Charts for Individual Observations. Quality and Reliability Engineering International, 27, 857-865.
  • Behseta, S. and Chenouri, S. (2011). Comparison of Two Population of Curves with an Application in Neuronal Data Analysis. Statistics in Medicine, 30, 1441-1454.
  • Mojirsheibani, M. and Chenouri, S. (2011). Classification When the Covariance Vectors have Unequal Dimensions. Journal of Statistical Planning and Inference, 141, 1944-1957.
  • Bagheri, M. Saccomanno, F. Chenouri, S. and Fu, L. (2011). Reducing the Threat of Derailments Involving Dangerous Goods Through Effective Placement Along the Train Consist". Accident Analysis and Prevention,  43, 613-620.
  • Akram, S. B., Frank, J. S. and Chenouri, S. (2010). Turning Behavior in Healthy Older Adults: Is There a Preference for Step Versus Spin Turns? Gait & Posture, 31 23-26.
  • Chenouri, S., Mojirsheibani, M. and Montazeri, Z. (2009). Empirical Measures for Incomplete Data with Applications. Electronic Journal of Statistics, 3, 1021-1038.
  • Chenouri, S., Mulaya Variath, A. and Steiner, S. (2009). A Robust Multivariate Control Chart for Individual Observations. Journal of Quality Technology, 41, 259-271.