澳门王中王论坛

Zeinab Mashreghi

Title: Associate Professor, Statistics
Phone: 204.786.9366
Office: 6L40
Building: Lockhart Hall
Email: z.mashreghi@uwinnipeg.ca

Degrees:

Ph.D. in Statistics, Université de Montréal.
M.Sc. in Statistics (Direct promotion to Ph.D program), Université de Montréal.
M.Sc. in Pure Mathematics, Université Laval.
B.Sc. in Applied Mathematics, University of Kashan, Iran.

Biography:

Zeinab received her master's degree in Pure Mathematics from Laval University. Due to her interests in Statistics, she pursued doctoral studies at the University of Montréal after initiating a second master's program in the same field. Her main research interests include sampling theory, particularly focusing on nonresponse, resampling methods, imputation, and variance estimation.

Dr. Zeinab Mashreghi

Affiliations:

Associate Professor, University of 澳门王中王论坛, Since July 2020
Adjunct Professor, Department of Community Health Sciences, University of Manitoba, Since October 2021
Assistant Professor, University of 澳门王中王论坛, January 2016-June 2020

Courses:

• Mathematical Statistics II (STAT/MATH-3612)
• Applied Regression Analysis (STAT-3103)
• Survey Sampling I (STAT-2301)
• Survey Sampling II (STAT-3302)
• Statistical Analysis I (STAT-1301)
• Statistical Analysis II (STAT-1302)
• Elementary Biological Statistics I (STAT-1501)

Research Interests:

Sampling Methodology, Nonresponse Handling, Bootstrap, Variance Estimation, R Package Development, and Infectious Disease Modelling.

Dr. Mashreghi currently holds an NSERC Grant, which allows her to support students in research positions. Contact her to learn about research opportunities.

Publications:

• Mashreghi, Z. (2024), The ‘bootsurv’ R Package,
• Abed, A., Torabi, M. and Mashreghi, Z. (2024). Gonorrhea Cluster Detection in Manitoba, Canada: Spatial, Temporal, and Spatio-temporal Analysis, Infectious Disease Modelling, 9(4), 1045--1056.
• Mashreghi Z. and Nasri M. (2024), Bregman Distance Regularization for Nonsmooth and Nonconvex Optimization, Canadian Mathematical Bulletin, 67(2), 415--424.
• Mashreghi, Z. and Deng, H. (2023). A Rescaling Bootstrap Approach for Imputed Survey Data. Journal of Survey Statistics and Methodology. 11(1): 234--259.
• Chen, S., Haziza, D. and Mashreghi, Z. (2022). A Comparison of Existing Bootstrap Algorithms for Multi-Stage Sampling Designs. Stats. 5(2): 521--537.
• Chen, S., Haziza, D. and Mashreghi, Z. (2021). Multiply Robust Bootstrap Variance Estimation in the Presence of Singly Imputed Survey Data. Journal of Survey Statistics and Methodology. 9(4): 810—832
• Chen S., Haziza, D., Léger, C. and Mashreghi, Z. (2019). Pseudo-population Bootstrap Methods for Imputed Survey Data. Biometrika, 106(2), 369–384.
• Mashreghi, Z., Haziza, D. and Léger, C. (2016). A Survey of Bootstrap Methods in Finite Population Sampling. Statistics Surveys, 10, 1–52.
• Mashreghi, Z., Léger, C. and Haziza, D. (2014). Bootstrap Methods for Imputed Data from Regression, Ratio and Hot deck Imputation. The Canadian Journal of Statistics, 42(1), 142–167.
• Mashreghi Z. (2010). Bootstrap Variance Estimation in the Presence of Imputed Data. Report Submitted to Statistics Canada at End of MITACS Internship.