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Forecasting binary longitudinal data by a functional PC-ARIMA model

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.
Year of publication:
2008
Authors: Aguilera, Ana M. ; Escabias, Manuel ; Valderrama, Mariano J.
Published in:
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 52.2008, 6, p. 3187-3197
Publisher: Elsevier
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Type of publication: Article
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Persistent link: https://www.econbiz.de/10005130983
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