Cornelia's research interests are on the intersection of (nonparametric) statistics and stochastic networks.
Abstract:
Nonparametric Estimation in Stochastic Networks Stochastic networks are interacting systems of nodes with service stations between which customers move in order to receive service. The talk addresses nonparametric estimation problems for these systems in discrete time. In particular we are interested in estimating the service time distributions at the nodes based on incomplete observations of the systems. We assume that we are only able to observe the external arrivals and departures of customers. We propose two estimation approaches. The first one is based on the construction of a so-called sequence of differences and the second utilizes the structure of cross-covariance functions between specific stochastic processes of the network. Both methods lead to deconvolution problems which we solve explicitly and which lead to consistent estimators. As main properties we show functional central limit theorems for the estimators in appropriate discrete spaces.
