Functional data featured by a spatial dependence structure occur in many environmental
sciences when curves are observed, for example, along time or along depth. Recently, some …

In a wide range of scientific fields the outputs coming from certain measurements often come
in form of curves. In this paper we give a solution to the problem of spatial prediction of non …

This paper addresses the introduction and study of structural properties of Hilbert-valued
spatial autoregressive processes (SARH (1) processes), and Hilbert-valued spatial moving …

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

Simultaneous autoregressive (SAR) models have been extensively used for the analysis of
spatial data in areas as diverse as demography, economy and geography. These are linear …

Spatio–temporal statistical models have been proposed for the analysis of the temporal
evolution of the geographical pattern of mortality (or incidence) risks in disease mapping …

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 …

This paper proposes a new methodology for detecting outliers in spatially correlated
functional data. We use a Local Correlation Integral (LOCI) algorithm substituting the …

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 …

High-frequency financial data can be collected as a sequence of time-ordered curves, such
as intraday prices. The Functional Data Analysis (FDA) framework offers a powerful …