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A random matrix perspective of high-dimensional data analysis
University of California, Davis
In this talk, we discuss how developments in random matrix theory (RMT) have influenced research in high-dimensional data analysis, including principal components analysis and factor analysis for complex data. We will mention several extensions of classical statistical procedures to high-dimensional settings that have been proposed by utilizing RMT. Recent developments, including the extension of the scope of RMT to deal with high-dimensional spatially and temporally dependent data models, will also be mentioned.
Friday September 20, 2013
Mesa Laboratory – Damon Room