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Saturday, May 19
Computational Statistics
Image and High-Dimensional Processing
Sat, May 19, 1:15 PM - 2:45 PM
Regency Ballroom A

Robust Analysis of High Dimensional Data (304471)

*Quefeng Li, UNC Chapel Hill 

Keywords: high dimensional data analysis, robust statistics

In the last decade, many new statisical tools have been developed to handle the large-p-small-n problem. However, most of these tools rely on the assumption that the underlying distribution is light-tailed (i.e. close to the Gaussian distribution). In the high dimensional setting, when many variables are involved, such an assumption is often too strong. In data collected from the real world, such as genomic data and neuroimaging data, we often observe outliers, skewness, and other aspects that clearly indicate that the underlying distribution is very different from Gaussian. Therefore, it is important to develop robust methods with guaranteed statistical properties for analyzing data that are collected from heavy-tailed distributions. In this talk, we will discuss the robust estimation of covariance/precision matrix and the robust linear regression under the high dimensional setting.