A0575
Title: Weighted conditional network testing for multiple high-dimensional correlated data sets
Authors: Takwon Kim - Seoul National University (Korea, South) [presenting]
Inyoung Kim - Virginia Tech (United States)
Ki-Ahm Lee - Seoul National University (Korea, South)
Abstract: Gaussian graphical models (GGMs) have been investigated to infer dependence (or network) structure among high-dimensional data by estimating a precision matrix. However, while many estimation methods for GGM have been developed, methods for testing the equality of two precision matrixes are still limited. Because testing the equality of the precision matrix depends on other given precision matrices, a weighted conditional network testing for considering other given precision matrices information is developed, and theoretical properties are also provided. None of the existing methods can be applied to test conditional differences when other networks are conditionally given and different. The advantage of the approach using a simulation study and genetic pathway analysis is demonstrated.
