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Geostatistical functional data analy...

Giraldo, Ramón,

Geostatistical functional data analysis /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Geostatistical functional data analysis /edited by Jorge Mateu, Ramón Giraldo.
other author:	Giraldo, Ramón,
Published:	Hoboken, NJ :Wiley,2022.
Description:	xvi, 432 p. :ill. ;24 cm.
Subject:	Functional analysis.
ISBN:	9781119387848 :

Geostatistical functional data analysis /
Geostatistical functional data analysis /edited by Jorge Mateu, Ramón Giraldo. - Hoboken, NJ :Wiley,2022. - xvi, 432 p. :ill. ;24 cm. - Wiley series in probability and statistics.

Includes bibliographical references and index.

Introduction to geostatistical functional data analysis -- Mathematical foundations of functional kriging in Hilbert spaces and Riemannian manifolds-- Universal, residual and external drift functional kriging -- Extending functional kriging when data are multivariate curves : some technical considerations and operational solutions -- Geostatistical analysis in Bayesspaces : probability densities and compositional data -- Spatial functionaldata analysis for probability density functions : compositional functional data vs distributional data approach -- Clustering spatial functional data-- Nonparametric statistical analysis of spatially distributed functional data -- A non parametric algorithm for spatially dependent functional data :bagging Voronoi for clustering, dimensional reduction and regression --Non-parametric inference for spatio-temporal data based on local null hypothesis testing for functional data -- A penalized regression model forspatial functional data with application to the analysis of the production of waste in Venice Province -- Quasi-maximum likelihood estimators for functional linear spatial autoregressive models -- Spatial prediction andoptimal sampling for multivariate functional random fields --Spatio-temporal functional data analysis -- A comparison of spatio-temporaland functional kriging approaches -- From spatio-temporal smoothing to functional spatial regression : a penalized approach.

"Spatial functional data (SFD) arises when we have functional data (curves or images) at each one of the several sites or areas of a region.Statistics for SFD is concerned with the application of methods for modeling this type of data. All the fields of spatial statistics (point patterns,areal data and geostatistics) have been adapted to the study of SFD. For example, in point patterns analysis, the functional mark correlation function is proposed as a counterpart of the mark correlation function; in areal data, analysis of a functional areal dataset consisting of population pyramids for 38 neighborhoods in Barcelona (Spain) has been proposed; and ingeostatistical analysis diverse approaches for kriging of functional data have been given. In the last few years, some alternatives have been adapted for considering models for SFD, where the estimation of the spatial correlation is of interest. When a functional variable is measured in sitesof a region, i.e. when there is a realisation of a functional random field (spatial functional stochastic process), it is important to test for significant spatial autocorrelation and study this correlation if present. Assessing whether SFD are or are not spatially correlated allows us toproperly formulate a functional model. However, searching in the literature,it is clear that amongst the several categories of spatial functional methods, functional geostatistics has been much more developed considering both new methodological approaches and analysis of a wide range of case studies covering a wealth of varied fields of applications"--

ISBN: 9781119387848 :NT$3142

LCCN: 2021015788Subjects--Topical Terms:

200585
Functional analysis.

LC Class. No.: QE33.2.S82 / G434 2021

Dewey Class. No.: 551.072/7

Geostatistical functional data analysis /
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020 $a 9781119387848 : $c NT$3142
020 $z 9781119387886 (epub)
020 $z 9781119387909 (adobe pdf)
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040 $a LBSOR/DLC $b eng $c DLC
042 $a pcc
050 0 0 $a QE33.2.S82 $b G434 2021
082 0 0 $a 551.072/7 $2 23
245 0 0 $a Geostatistical functional data analysis / $c edited by Jorge Mateu, Ramón Giraldo.
260 $a Hoboken, NJ : $b Wiley, $c 2022.
300 $a xvi, 432 p. : $b ill. ; $c 24 cm.
490 0 $a Wiley series in probability and statistics
504 $a Includes bibliographical references and index.
505 0 $a Introduction to geostatistical functional data analysis -- Mathematical foundations of functional kriging in Hilbert spaces and Riemannian manifolds-- Universal, residual and external drift functional kriging -- Extending functional kriging when data are multivariate curves : some technical considerations and operational solutions -- Geostatistical analysis in Bayesspaces : probability densities and compositional data -- Spatial functionaldata analysis for probability density functions : compositional functional data vs distributional data approach -- Clustering spatial functional data-- Nonparametric statistical analysis of spatially distributed functional data -- A non parametric algorithm for spatially dependent functional data :bagging Voronoi for clustering, dimensional reduction and regression --Non-parametric inference for spatio-temporal data based on local null hypothesis testing for functional data -- A penalized regression model forspatial functional data with application to the analysis of the production of waste in Venice Province -- Quasi-maximum likelihood estimators for functional linear spatial autoregressive models -- Spatial prediction andoptimal sampling for multivariate functional random fields --Spatio-temporal functional data analysis -- A comparison of spatio-temporaland functional kriging approaches -- From spatio-temporal smoothing to functional spatial regression : a penalized approach.
520 $a "Spatial functional data (SFD) arises when we have functional data (curves or images) at each one of the several sites or areas of a region.Statistics for SFD is concerned with the application of methods for modeling this type of data. All the fields of spatial statistics (point patterns,areal data and geostatistics) have been adapted to the study of SFD. For example, in point patterns analysis, the functional mark correlation function is proposed as a counterpart of the mark correlation function; in areal data, analysis of a functional areal dataset consisting of population pyramids for 38 neighborhoods in Barcelona (Spain) has been proposed; and ingeostatistical analysis diverse approaches for kriging of functional data have been given. In the last few years, some alternatives have been adapted for considering models for SFD, where the estimation of the spatial correlation is of interest. When a functional variable is measured in sitesof a region, i.e. when there is a realisation of a functional random field (spatial functional stochastic process), it is important to test for significant spatial autocorrelation and study this correlation if present. Assessing whether SFD are or are not spatially correlated allows us toproperly formulate a functional model. However, searching in the literature,it is clear that amongst the several categories of spatial functional methods, functional geostatistics has been much more developed considering both new methodological approaches and analysis of a wide range of case studies covering a wealth of varied fields of applications"-- $c Provided by publisher.
650 0 $a Functional analysis. $3 200585
650 0 $a Geology $x Statistical methods. $3 191971
650 0 $a Kriging. $3 205876
650 0 $a Spatial analysis (Statistics) $3 185012
700 1 $a Giraldo, Ramón, $e editor. $3 913820
700 1 $a Mateu, Jorge, $3 709801
776 0 8 $i Online version: $t Geostatistical functional data analysis $d Hoboken, NJ :Wiley, 2021 $z 9781119387909 $w (DLC) 2021015789