We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed
to simulate the long-time dynamics of open quantum systems. The bootstrap method is …

The machine learning approaches are applied in the dynamical simulation of open quantum
systems. The long short-term memory recurrent neural network (LSTM-RNN) models are …

We propose a novel model selection algorithm based on a penalized maximum likelihood
estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These …

Abstract The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a
multipurpose flexible HPC cluster designed and operated by a collaboration between the …

Prediction of particulate matter concentrations is of particular interest in the field of air
pollution control. We focus on the spatio-temporal geostatistical approach to predicting …

This article gives a narrative overview of what constitutes climatological data and their
typical features, with a focus on aspects relevant to statistical modeling. We restrict the …

We address the problem of characterizing spatially variable Natural Background Levels
(NBLs) of concentrations of chemical species of environmental concern in a large-scale …

Spatial econometric models estimated on the big geo‐located point data have at least two
problems: limited computational capabilities and inefficient forecasting for the new out‐of …

Spatial mapping of biodiversity is crucial to investigate spatial variations in natural
communities. Several indices have been proposed in the literature to represent biodiversity …