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Forecasting binary longitudinal data by a functional PC-ARIMA model

  • Aguilera, Ana M.
  • Escabias, Manuel
  • Valderrama, Mariano J.
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    In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4PRYFRM-2/1/61f6489a0cd38eb25b86353cf9981a0c
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 52 (2008)
    Issue (Month): 6 (February)
    Pages: 3187-3197

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    Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3187-3197
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. Escabias, M. & Aguilera, A.M. & Valderrama, M.J., 2007. "Functional PLS logit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4891-4902, June.
    2. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2005. "Forecast comparison of principal component regression and principal covariate regression," Econometric Institute Research Papers EI 2005-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    4. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    5. Francisco Ocaña & Ana Aguilera & Manuel Escabias, 2007. "Computational considerations in functional principal component analysis," Computational Statistics, Springer, vol. 22(3), pages 449-465, September.
    6. Cristian Preda & Gilbert Saporta & Caroline Lévéder, 2007. "PLS classification of functional data," Computational Statistics, Springer, vol. 22(2), pages 223-235, July.
    7. Agustín Maravall, 2005. "An application of the Tramo Seats automatic procedure; direct versus indirect adjustment," Banco de Espa�a Working Papers 0524, Banco de Espa�a.
    8. Preda, C. & Saporta, G., 2005. "PLS regression on a stochastic process," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 149-158, January.
    9. Manteiga, Wenceslao Gonzalez & Vieu, Philippe, 2007. "Statistics for Functional Data," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4788-4792, June.
    10. Preda, C. & Saporta, G., 2005. "Clusterwise PLS regression on a stochastic process," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 99-108, April.
    11. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2006. "Using principal components for estimating logistic regression with high-dimensional multicollinear data," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1905-1924, April.
    12. Ana Aguilera & Francisco Ocaña & Mariano Valderrama, 1999. "Forecasting with unequally spaced data by a functional principal component approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 8(1), pages 233-253, June.
    13. Ombao, Hernando & Ringo Ho, Moon-ho, 2006. "Time-dependent frequency domain principal components analysis of multichannel non-stationary signals," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2339-2360, May.
    14. Bouzas, P.R. & Valderrama, M.J. & Aguilera, A.M. & Ruiz-Fuentes, N., 2006. "Modelling the mean of a doubly stochastic Poisson process by functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2655-2667, June.
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