Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
ANÁLISIS EXPLORATORIO DE VARIABLES REGIONALIZADAS CON MÉTODOS FUNCIONALES
EXPLORATORY ANALYSIS OF REGIONALIZED VARIABLES WITH FUNCTIONAL METHODS
Keywords:
análisis de datos funcionales, análisis en componentes principales funcional, estacionariedad (es)Functional data analysis, Functional principal components analysis, Stationarity (en)
Downloads
1Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de Estadística, Bogotá.
Profesor asociado. E-mail: rgiraldoh@unal.edu.co
Se muestra cómo las estadísticas descriptivas funcionales y el análisis en componentes principales funcional (ACPF) pueden emplearse en la evaluación empírica del supuesto de estacionariedad considerado en la modelación de variables regionalizadas. Se toma como ejemplo información georreferenciada correspondiente a mediciones de profundidad recogidas en 114 sitios de la Ciénaga Grande de Santa Marta, Colombia.
Palabras clave: análisis de datos funcionales, análisis en componentes principales funcional, estacionariedad.
It is shown how summary statistics of functional data and functional principal components analysis (FPCA) can be used to evaluate the stationarity assumption considered in modeling of regionalized variables. As an example is taken georeferenced information of depth measured at 114 locations at Ciénaga Grande de Santa Marta, Colombia.
Key words: Functional data analysis, Functional principal components analysis, Stationarity.
Texto completo disponible en PDF
Referencias
1. Cardot, H., Ferraty, F. & Sarda, P. (1999), Functional Linear Model, Statistics and Probability Letters 45, 1122.
2. Christakos, G. (2000), Modern Spatio Temporal Geostatistics, Oxford University Press, New York.
3. Cressie, N. (1993), Statistic for Spatial Data, John Wiley & Sons, New York.
4. Cuevas, A., Febrero, M. & Fraiman, R. (2004), An ANOVA Test for Functional Data, Computational Statistics and Data Analysis 47, 111122.
5. Delicado, P. (2007), Functional k-Sample Problem when Data are Density Functions, Computational Statistics Published online, http://www.Springerlink.com
6. Deville, J. (1974), Méthodes statistiques et numeriques de lanalyse harmonique, Ann. Insee 15, 3104.
7. Escabias, M., Aguilera, A. & Valderrama, M. (2004), Principal Components Estimation of Functional Logistic Regression: Discussion of Two Different Approaches, Journal of non Parametric Statistics 16(3-4), 365384.
8. Ferraty, F. & Vieu, P. (2006), Non Parametric Functional Data Analysis. Theory and Practice, Springer, New York.
9. Giraldo, R., Troncoso, W., Mancera, J. & Méndez, N. (2000), Geoestadística: una herramienta para la modelación en estuarios, Rev. Acad. Col. Cienc. 24(90), 6072.
10. He, G., Muller, G. & Wang, J. (2000), Extending Correlation and Regression from Multivariate to Functional Data, in M. Puri, ed., Asymptotics in Statistics and Probability, Brill Academic Publisher, Leiden, pp. 114.
11. Isaaks, E. & Srivastava, M. (1987), Applied Geostatistics, Oxford University Press, New York.
12. Journel, A. & Huijbregts, C. (1978), Mining Geostatistics, Academic Press, London.
13. Pezulli, S. & Silverman, B. (1993), Some Properties of Smoothed Components Analysis for Functional Data, Computational Statistics 8, 116.
14. R Development Core Team (2006), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. *http://www.R-project.org
15. Ramsay, J. (1998), Estimating Smooth Monotone Functions, Journal Royal Statistical Society, Series B 60, 365375.
16. Ramsay, J. & Dalzell, C. (1991), Some Tools for Functional Data Analysis, Journal Royal Statistical Society 53(3), 539572.
17. Ramsay, J. & Silverman, B. (1997), Functional Data Analysis, Springer.
18. Ramsay, J. & Silverman, B. (2005), Functional Data Analysis, Springer.
19. Samper, F. & Carrera, J. (1993), Geoestadística. Aplicaciones a la hidrogeología subterránea, Centro Internacional de Métodos Numéricos en Ingeniería, UPC Barcelona.
20. Simonoff, J. (1996), Smoothing Methods in Statistics, Springer.
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
Article abstract page views
Downloads
License
Copyright (c) 2007 Revista Colombiana de Estadística
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
