Forecasting binary longitudinal data by a functional PC-ARIMA model Aguilera Del Pino, Ana María Escabias Machuca, Manuel Valderrama Bonnet, Mariano José Logistic regression Functional principal component analysis ARIMA modelling 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. 2022-02-24T09:31:44Z 2022-02-24T09:31:44Z 2008-02-20 info:eu-repo/semantics/article Ana M. Aguilera, Manuel Escabias, Mariano J. Valderrama, Forecasting binary longitudinal data by a functional PC-ARIMA model, Computational Statistics & Data Analysis, Volume 52, Issue 6, 2008, Pages 3187-3197, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2007.09.015 http://hdl.handle.net/10481/72990 https://doi.org/10.1016/j.csda.2007.09.015 eng http://creativecommons.org/licenses/by-nd/3.0/es/ info:eu-repo/semantics/embargoedAccess Atribución-SinDerivadas 3.0 España Elsevier