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Название: Geostatistical Functional Data Analysis
Автор: Jorge Mateu, Ramon Giraldo
Издательство: Wiley
Год: 2022
Страниц: 451
Язык: английский
Формат: pdf (true)
Размер: 16.0 MB

Explore the intersection between geostatistics and functional data analysis with this insightful new reference.

Geostatistical Functional Data Analysis presents a unified approach to modelling functional data when spatial and spatio-temporal correlations are present. The Editors link together the wide research areas of geostatistics and functional data analysis to provide the reader with a new area called geostatistical functional data analysis that will bring new insights and new open questions to researchers coming from both scientific fields. This book provides a complete and up-to-date account to deal with functional data that is spatially correlated, but also includes the most innovative developments in different open avenues in this field.

Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces, or anything else varying over a continuum. In its most general form, under an FDA framework each sample element is a function. The continuum over which these functions are defined is often time, but may also be spatial location, wavelength, probability, etc.

Modern technology has made it possible to obtain large spatial and spatiotemporal data sets, and poses the challenge of statistical modeling of such data. The combination of spatial statistics with FDA has emerged as a key approach. This book presents new theories and methods to define, describe, characterize, and model functional data indexed in spatial or spatio-temporal domains. The main focus is on functional data obtained under a geostatistical framework, where the domain is fixed and continuous. Specific topics considered include kriging, clustering, regression, and optimal sampling, moving on in the last part of the book to spatiotemporal data. Some chapters also consider the treatment of functional data on lattices.

Containing contributions from leading experts in the field, this practical guide provides readers with the necessary tools to employ and adapt classic statistical techniques to handle spatial regression. The book also includes:

A thorough introduction to the spatial kriging methodology when working with functions
A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations
Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region
In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysis

Aimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, Geostatistical Functional Data Analysis will also prove to be a powerful addition to the libraries of geoscientists, environmental scientists, and economists seeking insightful new knowledge and questions at the interface of geostatistics and functional data analysis.









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