STAT-6344: Modeling for High Dimensional and Sparse Data

Description: Statistical methods and modern computational methods for analyzing high dimensional data and sparse data, methods applied to complex data structures in various fields (e.g., genomics, epidemiology, and data mining), screening tools and matrix approximation, modeling strategies for high dimensional sparse data (parametric, nonparametric, and semiparametric regression models), statistical inference, graphical modeling methods, signal approximation methods, method limitations, functional analysis, causal inference, and data integration.

Pathways: N/A

Course Hours: 3 credits

Corequisites: N/A

Crosslist: N/A

Repeatability: N/A