Phyllis Wan

Dr. Phyllis Wan is an Associate Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research lies at the intersection of statistical theory and applied econometrics, with a strong emphasis on extreme value analysis, time series modeling, and clustering techniques for rare events.

• Institution: Erasmus University Rotterdam
• School: Erasmus School of Economics
• Department: Econometrics
• Position: Associate Professor

Her research interests span Extreme Value Theory, Time Series Analysis, Nonparametric and Parametric Estimation, K-Means Clustering of Extremes, and Goodness-of-Fit Testing. She applies these methods to diverse domains including earthquake risk modeling, urban development forecasting, and network data analysis. Her work combines rigorous theoretical development with practical applications in risk assessment and forecasting.

The recent publications demonstrate a consistent focus on developing and validating statistical tools for modeling extreme events. These works span applications in seismology, urban planning, and network science, unified by a core methodology rooted in extreme value statistics, distance-based inference, and clustering of tail events. There is a clear trend toward interdisciplinary applications of advanced econometric techniques.

Dr. Wan has not been listed with any scientific awards in the provided text.

She has co-authored multiple peer-reviewed articles in high-impact journals such as Journal of Econometrics, Extremes, and International Journal of Forecasting, often collaborating with leading researchers in statistics and econometrics. While specific grant funding or student supervision details are not mentioned, her publication record indicates active research leadership and academic collaboration. Her ORCID profile confirms ongoing scholarly output and engagement.

There is no mention of specific labs, research groups, or teams in the provided text, though her collaborative publications suggest participation in broader research networks in statistics and econometrics.