Markov neighborhood regression for statistical inference of high-dimensional generalized linear models

Abstract

High-dimensional inference is one of fundamental problems in modern biomedical studies. However, the existing methods do not perform satisfactorily. Based on the Markov property of graphical models and the likelihood ratio test, this article provides a simple justification for the Markov neighborhood regression method such that it can be applied to statistical inference for high-dimensional generalized linear models with mixed features. The Markov neighborhood regression method is highly attractive in that it breaks the high-dimensional inference problems into a series of low-dimensional inference problems. The proposed method is applied to the cancer cell line encyclopedia data for identification of the genes and mutations that are sensitive to the response of anti-cancer drugs. The numerical results favor the Markov neighborhood regression method to the existing ones.