Modern studies from a variety of fields record multiple functional observations according to
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …

Functional data analysis (FDA) is a relatively new branch in statistics. Experiments where a
complete function is observed for each individual give rise to functional data. In this work we …

A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space Page 1
Electronic Journal of Statistics Vol. 7 (2013) 2209–2240 ISSN: 1935-7524 DOI: 10.1214/13-EJS843 …

This article presents an overview of methodologies for spatial prediction of functional data,
focusing on both stationary and non-stationary conditions. A significant aspect of the …

The prevention of flood risks and the effective planning and management of water resources
require river flows to be continuously measured and analyzed at a number of stations. For a …

The class of spatial autoregressive Hilbertian models (SARH (1) processes) is considered.
The projection estimation methodology proposed here is based on the biorthogonal …

A functional-data approach to the Argo data Page 1 The Annals of Applied Statistics 2022, Vol.
16, No. 1, 216–246 https://doi.org/10.1214/21-AOAS1477 © Institute of Mathematical Statistics …

This paper addresses the problem of spatial functional extrapolation in the framework of
spatial autoregressive Hilbertian processes of order one (SARH (1) processes) introduced in …

In environmental surveillance, cluster detection of environmental black spots is of major
interest due to the adverse health effects of pollutants, as well as their known synergistic …

Abstract Spatial Functional Statistics has emerged as a powerful tool in the spatial and
spatiotemporal analysis of data arising, for example, from Agriculture, Geology, Soils …