Mehdi Dagdoug
Assistant Professor of Statistics
Department of Mathematics and Statistics
McGill University
- Survey sampling
- Missing data
- Statistical learning
- High-dimensional inference
I am interested in statistical inference from survey data, both for finite population quantities and for superpopulation quantities, particularly in difficult settings: a large number of auxiliary variables, domains containing few sampled units, or nonresponse. Survey data are often neither independent nor identically distributed. I aim to understand how classical methods behave in this setting and how new tools, including machine learning, can improve them, by studying their asymptotic properties, how their uncertainty can be quantified, and when they are optimal.
This leads me to work on machine learning for finite population inference, statistical learning from survey data, missing data and high-dimensional inference.
mehdi.dagdoug@mcgill.ca Burnside Hall, room 1230
Teaching this year
- MATH 598 · Asymptotic Statistics (Fall 2026)
- MATH 324 · Statistics (Winter 2027)
Office & contact
Burnside Hall, room 1230
805 rue Sherbrooke Ouest, Montréal, QC H3A 0B9
Elsewhere
About
I am an Assistant Professor in the Department of Mathematics and Statistics at McGill University. Previously, I was a Postdoctoral Fellow at the University of Ottawa, working with David Haziza.
I completed my Ph.D. in Mathematics at the Laboratoire de Mathématiques de Besançon (Université de Bourgogne Franche-Comté), supervised by Camelia Goga and David Haziza. I defended my thesis, Statistical learning for high-dimensional sampling, on July 12, 2022. My doctoral work was funded by the Région Franche-Comté and Médiamétrie.
Recent papers
- Agnostic model-assisted estimation with machine learning for survey data
- High-dimensional variance estimation for the generalized regression estimator
- Machine learning methods for finite population parameter estimation in survey sampling
- Variable selection for linear regression imputation in surveys
Recent talks
- Aug 2026 Agnostic model-assisted estimation in finite population
- Aug 2026 Finite-population inference with ML-based predictions
- Jun 2026 Machine learning for the treatment of nonresponse in surveys
- May 2026 Finite-population inference with ML-based predictions