Geometric Inference for General High-Dimensional Linear Inverse Problems
主 题: Geometric Inference for General High-Dimensional Linear Inverse Problems
报告人: Prof. Tony Cai (University of Pennsylvania)
时 间: 2015-12-07 14:00 - 15:00
地 点: 理科一号楼 1114
In this talk, we present a unified theoretical framework for the analysis of a general ill-posed linear inverse model which includes as special cases high-dimensional linear regression, sign vector recovery, trace regression, orthogonal matrix estimation, and noisy matrix completion. We propose computationally feasible convex programs and develop a theoretical framework to characterize the local rate of convergence for estimation accuracy, and to provide statistical inference procedures including confidence intervals and hypothesis tests for parameters. The unified theory is built based on the local conic geometry and duality
