A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space

By Alessandra Menafoglio, Piercesare Secchi, Matilde Dalla Rosa. 2013

118 citations recorded by the source corpus.

: We address the problem of predicting spatially dependent functional data belonging to a Hilbert space, with a Functional Data Analysis approach. Having defined new global measures of spatial variability for functional random processes, we derive a Universal Kriging predictor for functional data. Consistently with the new established theoretical results, we develop a two-step procedure for predicting georeferenced functional data: first model selection and estimation of the spatial mean (drift), then Universal Kriging prediction on the basis of the identified model. The proposed methodology is applied to daily mean temperatures curves recorded in the Maritimes Provinces of Canada.

Subfields: Economics and Econometrics, Environmental Engineering, Genetics

View Paper: https://www.semanticscholar.org/paper/1eb466482a995f76af56987cba9e5ef774422b5c

Citations in the corpus

• A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province (2016, unknown): “Recent techniques developing universal kriging approaches for spatially dependent functional data are offered by Caballero et al (2013), Menafoglio et al (2013) and Menafoglio et al (2014).”