A0697
Title: Large scale partial correlation screening
Authors: Peter Radchenko - University of Sydney (Australia) [presenting]
Abstract: Identifying multivariate dependencies in high-dimensional data is an important problem in large-scale inference. This problem has motivated recent advances in mining (partial) correlations, focusing on the challenging ultra-high dimensional setting where the sample size $n$ is fixed while the number of features $p$ grows without bounds. A novel principled framework for partial correlation screening with error control will be discussed, leveraging the connection between partial correlations and regression coefficients. Inferential properties of the proposed approach will be established when $n$ is fixed, and $p$ grows to infinity. The theory and methods will be validated on simulated and real data.
