A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
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CINAR random fields extend INAR models by decoupling the marginal distribution (chosen from discrete self-decomposable laws) from the autoregressive dependence structure.
Survey of thinning-based INARMA models for count random fields on regular 2D grids, covering thinning operators, model orders, and unilateral/multilateral structures.
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Tests for Independence of High-Dimensional Nonstationary Time Series
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
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INARMA Models for Count Random Fields -- a Survey
Survey of thinning-based INARMA models for count random fields on regular 2D grids, covering thinning operators, model orders, and unilateral/multilateral structures.