This paper proposes a Bayesian estimation framework for panel-data sets with binary dependent variables where a large number of cross-sectional units is observed over a short period of time, and cross-sectional units are interdependent in more than a single network domain. The latter provides...

We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data...

This paper derives a finite-sample oracle inequality for a class of misspecified multivariate conditional autoregressive quantile forecasts. This inequality is used to establish that the M-estimator of a multivariate version of the conditional autoregressive Value-at-Risk model (CAViaR) achieves...

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction both in the low- and high-dimensional set-up. To account for whole-day market dynamics in the financial market, the proposed model has two different instantaneous volatility...

In underground mining, the tunnels are performed mainly by blasting. Unfortunately, their quality may be different than expected. It is important to monitor both the quality and changes of existing tunnels. The length of corridors in underground mines is massive. Even if one wants to limit...

In this paper, we present a unified statistical model for multivariate and multi-modal sub-band distribution in multi-resolution transforms of color-texture images. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables accurate...

This paper proposes a novel event-based simulation model for assessing the explicit demand response potential of electric vehicle (EV) charging networks. The model utilizes multivariate copulas in generation of realistic artificial charging events that retain the complex dependency structures...