Neural Network for Asymptotic Dependence/ Independence Classification: A Series of Experiments

Abstract An early choice in the modeling of multivariate extremes is to infer whether the data are asymptotically dependent (AD) or asymptotically independent (AI). We perform a series of experiments to determine whether a convolutional neural network can reliably distinguish between these asymptotically defined regimes in the finite sample bivariate case. Along the way we develop a new classification tool for practitioners which we call \texttt{nnadic} as it is a Neural Network for Asymptotic Dependence/ Independence Classification. This tool accurately classifies 95% of test datasets and is robust to a wide range of sample sizes. The datasets which we are unable to correctly classify tend to either be nearly exactly independent or exhibit near perfect dependence, which are boundary cases for both the AD and AI models used for training.