Published
2010-08-28

Uso de modelos aditivos para evaluar la variabilidad espacial y temporal en datos 3D

Use of Additive Models to Assess the Time and Space Variability in 3D Data

DOI: https://doi.org/10.15332/s2027-3355.2010.0002.02
Francisco Andrés Rincón Rodríguez
Subgerente de riesgo. HSBC Colombia.
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Abstract (en)

This paper is aiming to identify the presence of variability over time and space for water temperature in Santa Marta, Colombia. The modeling process consider an approach through linear models, the use of additive models as an alternative to capture nonlinear patterns, evaluation of the need for a non parametric effect for each of the covariates and finally, a diagnostic test for the residuals to assess the need to include a covariance structure over time and/or space.
Keywords (en): Efecto no paramétrico, estructura de covarianza sobre el tiempo y espacio, modelos aditivos, variabilidad

Abstract (es)

Este articulo está dirigido a identificar la presencia de variabilidad sobre tiempo y espacio para la temperatura del agua en Santa Marta, Colombia. El proceso de modelamiento considera una aproximación a través de un modelo lineal, el uso de modelos aditivos como una alternativa para capturar patrones no lineales, evaluación de la necesidad de un efecto no paramétrico para cada una de las covariables y finalmente un diagnóstico sobre los residuales para valorar la necesidad de incluir una estructura de covarianza sobre tiempo y/o espacio.
Keywords (es): Efecto no paramétrico, estructura de covarianza sobre el tiempo y espacio, modelos aditivos, variabilidad

References

Beare, D. & Reid, D. (2002), ‘Investigating spatial-temporal change in spawning activity by Atlantic mackerel between 1977 and 1998 using generalized additive models’, ICES Journal of Marince Science, 59: 711-724 .

Bowman, A. & Azzalini, A. (1997), Applied Smoothing Techniques for Data Ana- lyis: the kernel approach with S-Plus illustrations, Oxford University Press.

Bowman, A., Giannitrapani, M. & Scott, E. (2009), ‘Spatial temporal smoothing and Sulphur dioxide trends over Europe’, Royal Statistical Society Series C 58, Part 5, pp 737-752.

Diblasi, A. & Bowman, A. (2001), ‘On the use of the variogram in checking for independence in spatial data’, Biometrics 57, 211-218.

Eastoe, E. F., Halsall, C. J., Heffernan, J. E. & Hung, H. (2006), ‘A statistical comparison of survival and replacement analyses for the use of censored data in a contaminant air database: A case of study from the Canadian arctic’, Atmospheric Environment 40, 6528-6540.

Esterby, S. (1993), ‘Trend analysis methods for environmental data’, Environ- metrics, 4(4), 459-481.

Fan, J. & Gijbels, I. (1996), Local polynomial modelling and its applications, Chap- man and Hall: London.

G.Bernal, Poveda, G., Roldan, P. & Andrade, C. (2006), ‘Patrones de variabilidad de las temperaturas superficiales del mar en la costa caribe colombiana’, Rev Acad Colom Cienc 30 (115): 195-208.

Giannitrapani, M., Bowman, A. & Scott, E. (2005), Additives models for correlated data with applications to air pollution monitoring, Technical report, University of Glasgow.

Green, P. & Silverman, B. (1994), Nonparametric Regression and Generalized Lin- ear Models: A Roughness Penalty Approach, Chapman and Hall: London.

Hastie, T. & Tibshirani, R. (1990), Generalized Additive Models, Monographs on Statistics and Applied Probability 43, Chapman and Hall.

IPCC (2007), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,.

Lewis, P. A. & Ray, B. K. (1997), ‘Modeling long-range dependence, nonlinearity, and periodic phenomena in sea surface temperatures usingtsmars’, Journal of the American Statistical Association, Vol 92, No 439, 881-893 .

McMullan, A., Bowman, A. & Scott, E. (2007), ‘Water quality in the river clyde: A case study of additive and interaction models’, Environmetrics.

Nadaraya, E. (1964), ‘Some new estimates for distribution functions’, Theory Probab. Appl. 9, 497-500.

NAOWA(Webpage) (n.d.), Electronic Resource [Accessed 22/11/2009].*http://www.naowa.com

Rincón, F. (2010), Statistical modelling of environmental trends over both time and space, Master’s thesis, University of Glasgow.

Watson, G. (1964), ‘Smooth regression analyses, Sankhya, Ser. A, 26, 359-72.

Wood, S. (2006), Generalized Additive Models: An Introduction with R, Chapman and Hall/CRC: London.

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How to Cite

Rincón Rodríguez, F. A. (2010). Use of Additive Models to Assess the Time and Space Variability in 3D Data. Comunicaciones En Estadística, 3(2), 119-132. https://doi.org/10.15332/s2027-3355.2010.0002.02

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