Forecasting binary longitudinal data by a functional PC-ARIMA model

Authors:
Ana M. Aguilera;Manuel Escabias;Mariano J. Valderrama
Affiliations:
Department of Statistics and O.R., University of Granada, Spain;Department of Statistics and O.R., University of Granada, Spain;Department of Statistics and O.R., University of Granada, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

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 Nino phenomenon.