Split variable selection for tree modeling on rank data

A variable selection method for constructing decision trees with rank data is proposed. It utilizes conditional independence tests based on loglinear models for contingency tables. Compared with other selection methods, our method is computationally more efficient. Moreover, our method is relatively unbiased and powerful in selecting the correct split variables. Simulation results and a real data study are given to demonstrate the strength of our method.