Functional SAR models: with application to spatial econometrics

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dc.contributor.authorPineda-Ríos, Wilmer
dc.contributor.authorGiraldo, Ramón
dc.contributor.authorPorcu, Emilio
dc.date.accessioned2019-06-18T22:07:06Z
dc.date.available2019-06-18T22:07:06Z
dc.date.issued2018-12-19
dc.description.abstractSimultaneous autoregressive (SAR) models have been extensively used for the analysis of spatial data in areas as diverse as demography, economy and geography. These are linear models with a scalar response, scalar explanatory variables and autoregressive errors. In this work we extend this modeling approach from scalar to functional covariates. Least squares and maximum likelihood are used as estimation methods of the parameters. A simulation study is considered for evaluating the performance of the proposed methodology. As an illustration, the model is used to establish the relationship between unsatisfied basic needs and curves of gross domestic product obtained in 32 departments of Colombia (districts of the country).spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdf
dc.identifier.citationPineda-Ríos, W., Giraldo, R., & Porcu, E. (2018). Functional SAR models: With application to spatial econometrics. Bogotá: doi:10.1016/j.spasta.2018.12.002spa
dc.identifier.doihttps://doi.org/10.1016/j.spasta.2018.12.002spa
dc.identifier.urihttp://hdl.handle.net/11634/17176
dc.publisher.branchCRAI-USTA Bogotáspa
dc.relation.referencesAguilera-Morillo, M.C., Durbán, M., Aguilera, A.M., 2017. Prediction of functional data with spatial dependence: a penalized approach. Stoch. Environ. Res. Risk Assess. 31 (1), 7–22.spa
dc.relation.referencesAhmed, M., 2017. Contribution à la statistique spatiale et l’analyse de données fonctionnelles (Ph.D. thesis), Department of Mathematics, University of Lille.spa
dc.relation.referencesBali, J.L., Boente, G., Tyler, D.E., Wang, J.-L., et al., 2011. Robust functional principal components: a projection-pursuit approach. Ann. Statist. 39 (6), 2852–2882.spa
dc.relation.referencesBox, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., 2015. Time series analysis: forecasting and control. John Wiley & Sons. Caballero, W., Giraldo, R., Mateu, J., 2013. A universal kriging approach for spatial functional data. Stoch. Environ. Res. Risk Assess. 27 (7), 1553–1563.spa
dc.relation.referencesCardot, H., Ferraty, F., Mas, A., Sarda, P., 2003. Testing hypotheses in the functional linear model. Scand. J. Stat. 30 (1), 241–255.spa
dc.relation.referencesChiou, J., Müller, H., Wang, J., 2004. Functional response models. Statist. Sinica 14, 675–693.spa
dc.relation.referencesDabo-Niang, S., Yao, F., 2007. Kernel regression estimation for continuous spatial processes. Math. Methods Statist. 16 (4), 298–317.spa
dc.relation.referencesDelicado, P., Broner, S., 2008. Distance-based LISA Maps for Multivariate Lattice Data. In: Technical Report, Universitat Politècnica de Catalunya, pp. 1–20.spa
dc.relation.referencesFox, J., 1984. Linear Statistical Models and Related Methods with Applications to Social Research. John Wiley & Sons.spa
dc.relation.referencesGiraldo, R., Caballero, W., Camacho-Tamayo, J., 2018. Mantel test for spatial functional data. Adv. Statist. Anal. 102 (1), 21–39.spa
dc.relation.referencesHaining, R., 1993. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press.spa
dc.relation.referencesHorváth, L., Kokoszka, P., 2012. Inference for Functional Data with Applications. Springer.spa
dc.relation.referencesJames, G., Hastie, T., 2001. Functional linear discriminant analysis for irregularly sampled curves. J. R. Stat. Soc. Ser. B Stat. Methodol. 63 (3), 533–550.spa
dc.relation.referencesOrd, K., 1975. Estimation methods for models of spatial interaction. J. Amer. Statist. Assoc. 70 (349), 120–126.spa
dc.relation.referencesPineda-Ríos, W., Giraldo, R., 2016. Functional SAR Model, ArXiv e-prints, 1609.03680, http://adsabs.harvard.edu/abs/2016arXi v160903680P.spa
dc.relation.referencesRamsay, J., Hooker, G., Graves, S., 2009. Functional Data Analysis with R and MATLAB. Springer Science & Business Media.spa
dc.relation.referencesSchabenberger, O., Gotway, C.A., 2005. Statistical Methods for Spatial Data Analysis. Chapman & Hall.spa
dc.relation.referencesTarpey, T., Kinateder, K., 2003. Clustering functional data. J. Classification 20 (1), 93–114.spa
dc.relation.referencesWaller, L., Gotway, C., 2004. Applied Spatial Statistics for Public Health Data. John Wiley & Sons.spa
dc.relation.referencesZhang, J., 2013. Analysis of Variance for Functional Data. CRC Press.spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.subject.keywordAutoregressive errorsspa
dc.subject.keywordFunctional linear modelsspa
dc.subject.keywordSpatial dependencespa
dc.subject.keywordSpatial weight matrixspa
dc.titleFunctional SAR models: with application to spatial econometricsspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

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