Forecasting binary longitudinal data by a functional PC-ARIMA model

Aguilera Del Pino, Ana María; Escabias Machuca, Manuel; Valderrama Bonnet, Mariano José

doi:https://doi.org/10.1016/j.csda.2007.09.015

Preprint Forecasting binary longitudinal data by a functional PC-ARIMA model (454.0Kb)

Identificadores

URI: http://hdl.handle.net/10481/72990

DOI: https://doi.org/10.1016/j.csda.2007.09.015

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Editorial

Elsevier

Materia

Logistic regression

Functional principal component analysis

ARIMA modelling

Fecha

2008-02-20

Referencia bibliográfica

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

Patrocinador

Projects MTM2007-63793 from Dirección General de Investigación, Ministerio de Educación y Ciencia, Spain and P06-FQM-01470 from Consejería de Innovación Ciencia y Empresa, Junta de Andalucía, Spain

Resumen

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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