作者：Rosset, Saharon; Tibshirani, Ryan J.
作者单位：Tel Aviv University; Carnegie Mellon University; Carnegie Mellon University
摘要：In statistical prediction, classical approaches for model selection and model evaluation based on covariance penalties are still widely used. Most of the literature on this topic is based on what we call the Fixed-X assumption, where covariate values are assumed to be nonrandom. By contrast, it is often more reasonable to take a Random-X view, where the covariate values are independently drawn for both training and prediction. To study the applicability of covariance penalties in this setting,...
作者：Tabouy, Tinnothee; Barbillon, Pierre; Chiquet, Julien
作者单位：AgroParisTech; Universite Paris Saclay; INRAE
摘要：This article deals with nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM). We review sampling designs and recover missing at random (MAR) and not missing at random (NMAR) conditions for the SBM. We introduce variants of the variational EM algorithm for inferring the SBM under various sampling designs (MAR and NMAR) all available as an R package. Model selection criteria based on integrated classification likelihood ar...
