Faculty - Dr. Sévérien Nkurunziza

Research Interests

My area of expertise is Statistics and my main research interests are statistical inference, statistical modeling, change-points, and applied probability. The main topics of my recent publications involve change-points, shrinkage estimation methods, tensor regression, stochastic modeling and inference in diffusion processes.

Supervision

I have supervised more than 10 undergraduate students and more than 30 graduate students including 3 PhD students. Past supervised students worked in statistical inference methods, inference in some tensor regression models, change-point modeling, inference in diffusion processes and their applications. My current and recently supervised graduate students have been working on robust inference methods as well as more applied topics such as neuroimaging modeling, inference in tensor regression models, change-points detection, inference in high-dimensional data, inference in mean-reverting processes. M.Sc. and Ph.D. students may select to work with me in topics such as statistical inference, inference in diffusion processes, model selection methods, asymptotic theory in statistics, change-points detection, and applied probability.

I. Current graduate students under supervision
1. Current PhD students

• Sathish Pichika
• Ran Sun
• Arash Foroushani

2. Current M.Sc students

• Dilay Gumus
• Lavanya Perera Dharmasena
• Yang Yang
• Dingshuo Li

II. Past Graduate students: 3 PhD and 28 MSc. The list is available on https://web2.uwindsor.ca/math/severien/PastSupervision.html.

Recent Publications

• Lyu, Y., and Nkurunziza, S. (2025). Estimation and Testing in Generalized CIR Model. Annals of Applied Probability, vol. 35(4), 2363--2410.
• Foroushani, A., A., and Nkurunziza, S. (2025). Improved Gaussian Mean Matrix Estimators in high-dimensional data. Journal of Multivariate Analysis, 208(105424), 1--19.
• Lyu, Y., and Nkurunziza, S. (2025). Inference methods in time-varying linear diffusion processes. Electronic Journal of Statistics,vol. 19(1), 1633--1680.
• Sun, R., Belalia, M. and Nkurunziza, S. (2025). The Empirical Bernstein Process with Application to Uniformity Testing. Statistical Papers, vol. 66 (48), 1--25.
• Lyu, Y., and Nkurunziza, S. (2024). Inference in Generalized Exponential OU Processes with Change-point. Statistical Inference for Stochastic Processes, vol. 27, 63--102.
• Ghannam, M., and Nkurunziza, S. (2024). Change-point Detection in a Tensor Regression Model. TEST, vol. 33, 609--630.
• Nkurunziza, S., and Li, Y. E. (2024). Improved Estimation in Multivariate Regression with Measurement Error. Journal of Statistical Computation and Simulation, vol. 94 (8), 1691--1714.
• Ghannam, M., and Nkurunziza, S. (2023). Tensor Stein-rules in a Generalized Tensor Regression Model. Journal of Multivariate Analysis, vol. 198, 1--20.
• Nkurunziza, S. (2023). Corrigendum: A note on Liu-type shrinkage estimations in linear models (Statistics 56, 396--420). Statistics, vol. 57 (4), 761--763.
• Lyu, Y., and Nkurunziza, S. (2023). Inference in Generalized Exponential O-U Processes. Statistical Inference for Stochastic Processes, Vol. 26, 581--618.
• Nkurunziza, S. (2023). On efficiency of some restricted estimators in a multivariate regression model. Statistical Papers, vol. 64, 617--642.
• Nkurunziza, S. (2023). Estimation and testing in multivariate generalized Ornstein-Uhlenbeck processes with change-points. Sankhya A, vol. 85, 351--400.
• Ghannam, M., and Nkurunziza, S. (2022). The risk of tensor Stein-rules in elliptically contoured distributions. Statistics, vol. 56 (2), 421--454.
• Ghannam, M., and Nkurunziza, S. (2022). Improved estimation in tensor regression with multiple change-points. Electronic Journal of Statistics, vol. 16 (2), 4162--4221.
• Nkurunziza, S. (2021). Inference problem in generalized fractional Ornstein-Uhlenbeck Processes with Change-Point. Bernoulli Journal, vol. 27 (1) 107--1.
• Nkurunziza, S., and Shen, L. (2020). Inference in a multivariate generalized mean-reverting process with a change-point. Statistical Inference for Stochastic Processes, vol. 23, 199--226.
• Chen, F., Mamon, R., and Nkurunziza, S. (2020). Inference for a change-point problem under an Ornstein-Uhlenbeck setting with unequal and unknown volatilities. The Canadian Journal of Statistics, vol. 48 (1), 62--78.
• Nkurunziza, S., and Fu, K. (2019). Improved inference in generalized mean-reverting processes with multiple change-points. Electronic Journal of Statistics, vol. 13 (1), 1400--1442.
• Chernoukhov, A, Hussein, A., Nkurunziza, S., and Bandyopadhyay, D. (2018). Bayesian inference in time-varying additive hazards models with applications to disease mapping. Environmetrics (Special Issue), vol. 29 (e2478), 1--10.
• Nkurunziza, S., and Zhang, P. P. (2018). Estimation and testing in generalized mean-reverting processes with change-point. Statistical Inference for Stochastic Processes, vol. 21, 1, 191--215.
• Chen, F., Mamon, R., and Nkurunziza, S. (2018). Inference for a change-point problem under a generalised Ornstein-Uhlenbeck setting. Annals of the Institute of Statistical Mathematics, vol. 70, 4, 807--853.
• Chen, F., and Nkurunziza, S. (2016). A class of Stein-rules in multivariate regression model with structural changes. Scandinavian Journal of Statistics, vol. 43, 1, 83--102.
• Chen, F., and Nkurunziza, S. (2015). Optimal method in Multiple Regression with Structural Changes. Bernoulli Journal, vol. 21, 4, 2217--2241
• Hussein, A. A., Nkurunziza, S.,and Tomanelli, K.(2014). Nonparametric Shrinkage estimation for Aalen's additive hazards model. Australian & New Zealand Journal of Statistics, vol. 56, 1, 15-26.
• Chen, F, and Nkurunziza, S. (2014). Constrained Inference in Multiple Regression with Structural Changes. Statistics & Risk Modeling (formerly Statistics & Decisions) vol. 31, 3-4, 237--257.
• Nkurunziza, S., and Chen, F. (2013). On extension of some identities for the bias and risk functions in elliptically contoured distributions. Journal of Multivariate Analysis, vol. 122, 190–201.
• Nkurunziza, S. (2013). Extension of Some Important Identities in Shrinkage-Pretest Strategies. Metrika, vol. 76, 937–947.
• Nkurunziza, S. (2013). The bias and risk functions of some Stein-rules in elliptically contoured distributions. Mathematical Methods of Statistics, vol. 22, 1, 70-82.
• Nkurunziza, S. (2012). Generalized Stein-Rule for drift parameter in diffusion processes. Annales de l’I.S.U.P., vol. 56, 1, 41-59.
• Nkurunziza, S. (2012). Shrinkage Strategies In Some Multiple Multi-factor Dynamical Systems. ESAIM: Probability and Statistics, vol. 16, 139-150.
• Nkurunziza, S. (2012). The Risk of Pre-Test and Shrinkage Estimators. Statistics: A Journal of Theoretical and Applied Statistics, vol. 46, 3, 305-312.
• Nkurunziza, S. (2012). A simple formula for asymptotic distributional risk of some estimators. Brazilian Journal of Probability and Statistics (BJPS), vol. 26, 2, 113-122.
• Nkurunziza, S., and Ahmed, S. E., (2011). Estimation Strategies for the Regression Coefficient Parameter Matrix in Multivariate Multiple Regression. Statistica Neerlandica, vol. 65, 4, 387-406.
• Nkurunziza, S. (2011). Shrinkage Strategy In Stratified Random Sample Subject To Measurement Error. Statistics and Probability Letters, vol. 81, 2, 317-325.
• Nkurunziza, S., and Ahmed, S. E. (2010). Shrinkage Drift Parameter Estimation for Multi-factor Ornstein-Uhlenbeck Processes. Applied Stochastic Models in Business and Industry, vol. 26, 2, 103-124.
• Ahmed, S. E., Hussein, A., and Nkurunziza, S. (2010). Robust Inference Strategy in the Presence of Measurements Error. Statistics and Probability Letters (SPL), vol. 80, 726-732.
• Nkurunziza, S. (2010). Testing Concerning the Homogeneity of Some Predator-Prey Populations. Journal of Statistical Planning and Inference (JSPI), vol. 140, 323-333.

For full list of publications https://web2.uwindsor.ca/math/severien/research.html. 

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