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Geostatistical functional data analysis
- Available as
- Online
- Summary
-
"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 ...
"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 in geostatistical 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 sites of 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 to properly 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"--Provided by publisher.
Details
- Creator
- edited by Jorge Mateu, Ramón Giraldo
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- Hoboken, NJ : John Wiley and Sons, Incorporated, 2022
- ©2022
- Physical Details
-
- 1 online resource : illustrations (chiefly color)
- data file
- ISBNs
- 9781119387916, 1119387914, 9781119387886, 1119387884, 1119387906, 9781119387909, 9781119387848, 9781119387848
- OCLC
- on1253442473
- 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 Bayes spaces : probability densities and compositional data -- Spatial functional data 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 for spatial 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 and optimal sampling for multivariate functional random fields -- Spatio-temporal functional data analysis -- A comparison of spatio-temporal and functional kriging approaches -- From spatio-temporal smoothing to functional spatial regression : a penalized approach
Subjects
- Geology -- Statistical methods.
- Kriging.
- Spatial analysis (Statistics)
- Functional analysis.
- spatial analysis.
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