Improving ramification detection of St. Nicolas House Analysis

The St. Nicolas House Analysis (SNHA) is a new graph estimation method for detection of extensive interactions among variables. It operates by ranking absolute bivariate correlation coefficients in descending order thereby creating hierarchic association chains. The latter characterizes dependence structures of interacting variables which can be visualized in a corresponding network graph as a chain of end-to-end connected edges representing direct relationships between the connected nodes. The important advantage of this relatively new approach is that it produces less false positive edges resulting from indirect or transitive associations than expected with standard correlation or linear model-based approaches. Here we aim to improve the detection of ramifications in graphs by addition of different data processing layers to SNHA. They include the combinations of the extensions R-squared gaining(RSG) and linear model check(LMC). SNHA together with these so-called extensions were benchmarked against default SNHA and other reference methods available for the programming language R. In the end combinations of RSG, LMC and Bootstrapping improve SNHA performance across different network types, albeit at the cost of longer computation time.