We introduce a model for a high-dimensional random graph process and ask how "rich" the process has to be so that one finds atypical behavior. In particular, we study a natural process of Erdös-Rényi random graphs indexed by unit vectors in R^d . We investigate the deviations of the process with respect to three fundamental…

The number of publicly available gene expression datasets has been growing dramatically. Various methods had been proposed to predict gene co-expression by integrating the publicly available datasets. These methods assume that the genes in the query gene set are homogeneously correlated and consider no gene-specific correlation tendencies, no background intra-experimental correlations, and no quality variations…

Professor Robins describes a novel approach to point and interval estimation of nonlinear functionals in parametric, semi-, and non-parametric models based on higher order influence functions. Higher order influence functions are higher order U-statistics. The approach applies equally to both n‾√ and non-n‾√ problems. It reproduces results previously obtained by the modern theory of non-parametric…

Missing data is a reality of empirical sciences and can rarely be prevented entirely. It is often assumed that incomplete data are missing completely at random (MCAR) or missing at random (MAR), When neither MCAR nor MAR, missingness is said to be Not MAR (NMAR). Under MAR, there are two main approaches to inference, likelihood/Bayesian…