The group traces its roots back nearly two decades ago when quantile regression gained prominence as a powerful tool for analyzing data in various disciplines. As interest in quantile regression grew, the group expanded to include a dynamic and interdisciplinary mix of faculty, researchers, Post-docs and PhD students from different institutions. We also collaborate with experts from various domains, fostering a collaborative environment that promotes cross-disciplinary insights.
The group is fully committed to expanding the theoretical foundations of quantile regression, constantly exploring innovative estimation techniques and refining existing methodologies. Beyond theoretical advancements, we are actively engaged in developing practical tools and software to facilitate the implementation of quantile regression in real-world scenarios. Over the years, the group has made major contributions to the development of advanced methods, techniques and novel applications in diverse fields, such as economics, finance, epidemiology, and environmental science. This comprehensive approach underscores the group’s commitment to exploring new avenues where quantile regression can offer unique insights into complex data structures.
Multivariate Quantiles
Machine Learning
Quantile Graphical Models
Mixed Frequency Data
Hidden Markov Models
Finite Mixture Models
Mixed-Effects Models
Longitudinal Data
Finance
Economics
Small Area Estimation
Poverty Mapping