An improvement of FDR for edge detection by applying EM method

Abstract

In building a graphical model, accuracy in edge detection for the model structure is crucial for the quality of the model. We explored methods for improvement of false discovery rate(FDR) by devising an estimation procedure which is more data sensitive under some condition. The estimation is made by applying an EM method where the parameters include the density function under the null hypothesis (no edge) and the location parameters of the density functions under the alternative hypothesis (presence of edge). Our method is compared favorably with a most popular FDR tool in numerical experiments. We applied our method for analysing gene data of 800 genes and built a network of vector autoregressive model for the data.