Statistics
Research Topics
- Statistical estimation and inference
- Likelihood methods
- Minimum distance methods
- Bayesian methods
- Methods for high-dimensional/big data
- Statistical learning
- Data mining and machine learning
- Classification and clustering
- Dimension reduction and feature selection
- Pattern recognition
- Experimental design
- Reliability and survival analysis
- Extreme-value analysis
- Stochastic process
- Statistical engineering
- Financial statistics
- Industrial statistics
Research Spotlight: Dr. Hua Shen
My research interests are on the methodology development and statistical analysis of data arising from public health and medical research. They include the analysis of survival data, recurrent events data, longitudinal data, incomplete data and other complex data involving multiple outcomes, measurement error, hierarchical structures and high dimension. My current research focuses on developing statistical methods to analyze incomplete lifetime data involving latent processes which often arise in clinical trials and observational studies.
I am interested in working with students at both graduate and undergraduate levels on statistics/biostatistics projects and research topics. Students with strong interests in statistics/biostatistics and solid background in statistics, biostatistics, applied mathematics, computer science and other related areas are encouraged to make inquiries.
I am also interested in supporting deep understanding and applying statistical principles through transdisciplinary partnership. Prospective collaborators from various areas are welcome to contact me for collaborative research.