A0457
Title: On sufficient graphical model
Authors: Kyongwon Kim - Ewha Womans University (Korea, South) [presenting]
Abstract: A sufficient graphical model is introduced by applying the recently developed nonlinear sufficient dimension reduction techniques to evaluate conditional independence. The graphical model is nonparametric in nature, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions. However, unlike a fully nonparametric graphical model, which relies on the high-dimensional kernel to characterize conditional independence, the graphical model is based on conditional independence, given a set of sufficient predictors with a substantially reduced dimension. In this way, the curse of dimensionality that comes with a high-dimensional kernel is avoided. The estimate's population-level properties, convergence rate, and variable selection consistency are developed. Simulation comparisons and an analysis of the DREAM 4 Challenge data set demonstrate that the method outperforms the existing methods when the Gaussian or copula Gaussian assumptions are violated. Its performance remains excellent in the high-dimensional setting.
