Speaker: Beomjo Park

Abstract: The asymptotic distribution of the MLE in logistic models based on the classical ML theory does not account for the bias and variance inflation, thus yields the incorrect inference if one uses LRT. Sur and Candes [SC19] proposed a theory to handle this issue in the high dimensional setting. Recently, the redux [ZSC20] came out to resolve the arbitrary (Gaussian) feature covariance. I'll more focus on the recent paper based on the stochastic representation but will also try to cover the overlapping ideas that arise in both papers.