Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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High-dimensional estimation and concentration phenomena Location: 0.002 Session Chair: Marie Düker | |
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Copula tensor count autoregressions 1: University of Rome Tor Vergata; 2: Vrije Universiteit Amsterdam This paper presents a novel copula-based autoregressive framework for multi-layer arrays of integer-valued time series with tensor structure. Our framework generalizes recent advances in tensor time series models for real-valued data to a context that accounts for the unique properties of integer-valued data, such as discreteness and non-negativity. The model incorporates feedback effects for the counts’ temporal dynamics and introduces identification constraints. An asymptotic theory is developed for a Two-Stage Maximum Likelihood Estimator (2SMLE) for the model’s parameters. The estimator balances the challenges of parameter dimensionality, interdependence of the different count series, and computational stability. Together, this substantially pushes the frontier for modeling multi-dimensional, structured tensor time series of counts. An application to tensor crime counts demonstrates the practical usefulness of the proposed methodology. | |
