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Penalized functional spatial regression

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  • Aguilera Morillo, María del Carmen
  • Durbán, María
  • Aguilera, Ana M.

Abstract

This paper is focus on spatial functional variables whose observa- tions are realizations of a spatio-temporal functional process. In this context, a new smoothing method for functional data presenting spa- tial dependence is proposed. This approach is based on a P-spline estimation of a functional spatial regression model. As alternative to other geostatistical smoothing methods (kriging and kernel smooth- ing, among others), the proposed P-spline approach can be used to estimate the functional form of a set of sample paths observed only at a finite set of time points, and also to predict the corresponding func- tional variable at a new location within the plane of study. In order to test the good performance of the proposed method, two simulation studies and an application with real data will be developed and the results will be compared with functional kriging.

Suggested Citation

  • Aguilera Morillo, María del Carmen & Durbán, María & Aguilera, Ana M., 2015. "Penalized functional spatial regression," DES - Working Papers. Statistics and Econometrics. WS 21206, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:21206
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    References listed on IDEAS

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    2. Eilers, Paul H.C. & Currie, Iain D. & Durban, Maria, 2006. "Fast and compact smoothing on large multidimensional grids," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 61-76, January.
    3. M. Aguilera-Morillo & Ana Aguilera & Manuel Escabias & Mariano Valderrama, 2013. "Penalized spline approaches for functional logit regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 251-277, June.
    4. Laura M. Sangalli & James O. Ramsay & Timothy O. Ramsay, 2013. "Spatial spline regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 681-703, September.
    5. Lee, Dae-Jin & Durbán, María, 2009. "Smooth-CAR mixed models for spatial count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2968-2979, June.
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