15.06.2022 12:15 Harry Joe (University of British Columbia, CAN):
Comparison of dependence graphs based on different functions of correlation matricesOnline: attendBC1 2.01.10 (Parkring 11, 85748 Garching)

A dependence graph for a set of variables has rules for which pairs of variables are connected. In the literature on dependence graphs for gene expression measurements, there have been several rules for connecting pairs of variables based on a correlation matrix: (a) absolute correlation of the pair exceed a threshold; (b) absolute partial correlation of the pair given the rest exceed a threshold; (c) first-order conditional independence rule of Magwene and Kim (2004).

These three methods will be compared with the dependence graph from a truncated partial correlation vine with thresholding. The comparisons are made for correlation matrices that are derived from (a) factor dependence structures, (b) Markov tree structure, and (c) variables that form groups with strong within group dependence and weaker between group dependence. If there are latent variables, the graphs are compared with and without them. The goal is to show that more parsimonious and interpretable graphs can be obtained with inclusion of latent variables.