Mehdi Dagdoug

Mehdi Dagdoug is an Assistant Professor in the Department of Mathematics and Statistics at McGill University. His research interests include survey sampling, statistical learning, and high-dimensional statistics. He is affiliated with the Mathematics and Statistics department and maintains a personal webpage at https://www.mcgill.ca/mathstat/mehdi-dagdoug. His work focuses on statistical methodologies with applications in various domains.

Mehdi Dagdoug is a researcher specializing in statistics, statistical learning, survey sampling, and missing data. His work focuses on applying machine learning techniques to address challenges in survey data analysis, such as unit and item nonresponse. He explores model-assisted estimation methods, including random forests and regression trees, for finite population sampling. His research also involves imputation procedures using nonparametric and machine learning approaches, with an emphasis on high-dimensional settings. Dagdoug's publications highlight the integration of statistical learning with survey sampling theory to improve variance estimation and data quality. His work bridges theoretical statistics with practical applications in survey methodology.