Geostatistical Functional Data Analysis - (Wiley Probability and Statistics) by Jorge Mateu & Ramon Giraldo (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"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"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>Geostatistical Functional Data Analysis</b> <p><b>Explore the intersection between geostatistics and functional data analysis with this insightful new reference</b> <p><i>Geostatistical Functional Data Analysis </i>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. <p>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: <ul><li>A thorough introduction to the spatial kriging methodology when working with functions</li> <li>A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations</li> <li>Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region</li> <li>In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysis</li></ul> <p>Aimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, <i>Geostatistical Functional Data Analysis</i> 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.<p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>Explore the intersection between geostatistics and functional data analysis with this insightful new reference</b></p> <p><i>Geostatistical Functional Data Analysis </i>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. <p>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: <ul><li>A thorough introduction to the spatial kriging methodology when working with functions</li> <li>A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations</li> <li>Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region</li> <li>In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysis</li></ul> <p>Aimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, <i>Geostatistical Functional Data Analysis</i> 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.<p/><br></br><p><b> About the Author </b></p></br></br><p><b>Jorge Mateu </b>is Full Professor of Statistics at the Department of Mathematics of University Jaume I of Castellon. His research focuses on stochastic processes with a particular interest in spatial and spatio-temporal point processes and geostatistics.</p> <p><b>Ramón Giraldo </b>is Full Professor of Statistics at the Department of Statistics at the Universidad Nacional de Colombia. His research focuses on non-parametric statistics, functional data analysis, and spatial and spatio-temporal geostatistics.
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