THE PHASE TRANSITION FOR THE EXISTENCE OF THE MAXIMUM LIKELIHOOD ESTIMATE IN HIGH-DIMENSIONAL LOGISTIC REGRESSION

This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp "phase transition." We introduce an explicit boundary curve h(MLE), parameterized by two scalars measuring the overall magnitude of the unknown sequence of regression coefficients, with the following property: in the limit of large sample sizes n and number of features p proportioned in such a way that p/n -> kappa, we show that if the problem is sufficiently high dimensional in the sense that kappa > h(MLE), then the MLE does not exist with probability one. Conversely, if kappa < h(MLE), the MLE asymptotically exists with probability one.