Introduces a Wasserstein-space multivariate autoregressive model for distributional time series, proving second-order stationarity via iterated random functions and providing a simplex-constrained consistent estimator that enables graph learning.
random objects taking values in a measurable spaceΘ, Φϵp¨q:“ Φp¨,ϵq is the ϵ-section of a jointly measurable functionΦ : Xˆ ΘÑ X
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Wasserstein multivariate auto-regressive models for modeling distributional time series
Introduces a Wasserstein-space multivariate autoregressive model for distributional time series, proving second-order stationarity via iterated random functions and providing a simplex-constrained consistent estimator that enables graph learning.