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We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Tim...
High-order harmonic generation (HHG) from the interaction of ultra-intense laser pulses with atoms is an important tabletop short-wave coherent light source. Accurate quantum simulations of it present...
Inspired by the study of air quality level data, we propose a new model for the normalcy-dominant ordinal time series. The proposed model is based on a new zero-one-inflated bounded Poisson distributi...
Inspired by the study of air quality level data, we propose a new model for the normalcy-dominant ordinal time series. The proposed model is based on a new zero-one-inflated bounded Poisson distributi...
The purpose of this workshop is to bring together the leading experts, active scholars and young researchers in Dynamic Network, Spatio-Temporal Processes, and Functional Time Series under the umbrell...

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