@misc{10481/72974,
year = {2012},
month = {10},
url = {http://hdl.handle.net/10481/72974},
abstract = {High levels of airborne olive pollen represent a
problem for a large proportion of the population because of
the many allergies it causes. Many attempts have been
made to forecast the concentration of airborne olive pollen,
using methods such as time series, linear regression, neural
networks, a combination of fuzzy systems and neural networks, and functional models. This paper presents a
functional logistic regression model used to study the
relationship between olive pollen concentration and different climatic factors, and on this basis to predict the
probability of high (and possibly extreme) levels of airborne pollen, selecting the best subset of functional climatic variables by means of a stepwise method based on
the conditional likelihood ratio test.},
organization = {Projects MTM2010-20502 from Dirección General de Investigación del MEC, Spain and FQM-307 from Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía Spain},
publisher = {Springer},
keywords = {Olea europaea L. airborne pollen},
keywords = {Functional-logit-regression},
keywords = {Selection of functional predictors},
title = {Stepwise selection of functional covariates in forecasting peak levels of olive pollen},
doi = {27. 10.1007/s00477-012-0655-0},
author = {Escabias Machuca, Manuel and Valderrama Bonnet, Mariano José and Aguilera Del Pino, Ana María and Santofimia, M. Elena and Aguilera Morillo, María del Carmen},
}