Jorge Mateu & Ramon Giraldo 
Geostatistical Functional Data Analysis 

Geostatistical Functional Data Analysis

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.

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.

Tabla de materias

Preface

1. Introduction to Geostatistical Functional Data Analysis

2. Mathematical foundations of functional Kriging in Hilbert spaces and Riemannian manifolds

3. Universal, Residual and External Drift Functional Kriging

4. Extending functional kriging when data are multivariate curves : some technical considerations and
operational solutions

5. Geostatistical analysis in Bayes spaces: probability densities and compositional data

6. Spatial functional data analysis for probability density functions: compositional functional data vs
distributional data approach

7. Clustering spatial functional data

8. Nonparametric statistical analysis of spatially distributed functional data

9. A non parametric algorithm for spatially dependent functional data: Bagging Voronoi for clustering,
dimensional reduction and regression

10. Non-parametric inference for spatio-temporal data based on local null hypothesis testing for functional data

11. A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province

12. Quasi-Maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models

13. Spatial Prediction and Optimal Sampling for Multivariate Functional Random Fields

14. Spatio-temporal Functional Data Analysis

15. A comparison of spatio-temporal and functional kriging approaches

16. From spatio-temporal smoothing to functional spatial regression: a penalized approach

Index

Sobre el autor

Jorge Mateu 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.

Ramón Giraldo 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.