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Research Areas

Statistical Theory and Methodology

We conduct fundamental statistical research related to theory and applications. Focus areas include but not limited to: high dimensional statistical inference, asymptotic theory, Bayesian inference, functional data analysis, longitudinal models, nonparametric methods, structural equation models, applied probability and stochastic analysis, financial modeling, time series analysis, and machine learning.

Actuarial Science, Mathematical Finance and Risk Management Science

The science of risk management has become an indispensable component of business, particularly for financial institutions such as commercial and investment banks and insurance and reinsurance companies. The Department plays a leading role in risk management science education and research in Hong Kong. Research topics include but not limited to: option pricing, credit risk modeling, actuarial science, financial objective optimization, and volatility modeling in financial time series.

Biostatistics and Bioinformatics

In collaborations with the Faculty of Medicine and School of Life Sciences, we research in areas like: genome-wide association studies for diabetes and cancer, copy number variation in cancer, gene network inference, sequence pattern detection, embryonic gene expression analysis, protein structure prediction, evolution modeling, clinical study design and analysis.

Statistics in Education, Policy and Social Sciences

Statistical inferences are drawn from diverse social science data sources coming from large surveys on educational and psychological experiments.

Big Data Analytics

The Department develops methodology and techniques for statistical learning from big data, including but not limited to genomic data, clinical data, financial data, insurance data, and transportation data.

Other Interdisciplinary and Collaborative Research

Statistics has become an important tool in a variety of quantitative fields, including biological and life sciences, medical science, finance, marketing, computer engineering, education and social science. The Department welcomes research collaborations with other faculties and departments.

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