Many scientific and economic applications involve the statistical learning of high-dimensional functional time series, where number variables is comparable to, or even greater than, serially dependent observations. In this paper, we model observed which are subject to errors in sense that each datum arises as sum two uncorrelated components, one dynamic white noise. Motivated from fact autocova...

In the era of ubiquitous data collection and generation, demands are high to make these accessible as widely possible, with little effort much power flexibility ever possible. On Earth data, this holds in particular for pixel point clouds, some main “Big Data” today. Coverages represent a unifying concept space/time-varying especially spatio-temporal gridded nowadays often called “datacubes". C...

We present a computational framework for estimating the uncertainty in the numerical solution of linearized infinite-dimensional statistical inverse problems. We adopt the Bayesian inference formulation: given observational data and their uncertainty, the governing forward problem and its uncertainty, and a prior probability distribution describing uncertainty in the parameter field, find the p...