Abstract: In this talk, we will study higher-order accuracy of the weighted bootstrap procedure for estimation of a distribution of a sum of independent random vectors with bounded fourth moments, on the set of all Euclidean balls. Our approach is based on Berry-Esseen type inequality which extends the classical normal approximation bound. These results justify in…

Abstract: We will present results in sparse linear regression on two convex regularized estimators, the Lasso and the recently introduced Slope estimator, in the high-dimensional setting where the number of covariates p is larger than the number of observations n. The estimation and prediction performance of these estimators will be presented, as well as a comparative study of the…

Abstract: While causal relations are generally considered to be anti-symmetric, we often find that over time there are feedback systems such that a variable can have a causal effect on itself. Such "cyclic" causal systems pose significant challenges for causal analysis, both in terms of the appropriate representation of the system under investigation, and for the development of…

Abstract:  Learning properties of large graphs from samples is an important problem in statistical network analysis, dating back to the early work of Goodman and Frank. We revisit the problem formulated by Frank (1978) of estimating the numbers of connected components in a graph of N vertices based on the subgraph sampling model, where we observe the…

Abstract: Partition functions are just multivariate polynomials with great many monomials enumerating combinatorial structures of a particular type and their efficient computation (approximation) are of interest for combinatorics, statistics, physics and computational complexity. I’ll present a general principle: the partition function can be efficiently approximated in a domain if it has no complex zeros in a slightly…