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Title: A vine copula change point model for neuroimaging studies and their computational reproducibility Authors:  Ivor Cribben - Alberta School of Business (Canada) [presenting]
Abstract: A new methodology called Vine Copula Change Point (VCCP) is introduced to estimate change points in the network structure between multivariate time series. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence, including tail, symmetric and asymmetric dependence, which has not been explored before in the dynamic analysis of neuroimaging data. We will also discuss some recent work on reproducibility in statistics by attempting to reproduce the results in 93 published papers in prominent journals utilizing functional magnetic resonance imaging (fMRI) data during the 2010-2021 period.