@misc{10481/72908,
year = {2014},
month = {9},
url = {http://hdl.handle.net/10481/72908},
abstract = {The power of functional linear regression to estimate a set of curves from others
involved is studied in this work in the context of life sciences. The objective is to determine
the relationship between the degree of lupus and the level of stress for patients suffering this
autoimmune disease. Daily stress and lupus curves have a strong local behavior with missing data those days that a patient does not answer the corresponding test. Because of this,
wavelet smoothing with an appropriate thresholding rule is considered. Then, functional
principal component analysis of the response and predictor variables is used to reduce the
dimension and solve the multicollinearity problem that affects the estimation of the functional linear regression model with functional response. Model selection is solved by using
a criterion that selects those pairs of response/predictor components that explain the highest proportions of response variability. The performance of the proposed functional model
is tested on simulated and real data.},
organization = {Project P11-FQM-8068 from Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía},
organization = {Project MTM2013-47929-P from Ministerio de Economía y Competitividad, Spain},
publisher = {Springer},
keywords = {Functional regression},
keywords = {Functional PCA},
keywords = {Wavelet approximation},
keywords = {Lupus},
title = {Functional Wavelet-Based Modelling of Dependence Between Lupus and Stress},
doi = {https://doi.org/10.1007/s11009-014-9424-5},
author = {Aguilera Del Pino, Ana María and Escabias Machuca, Manuel and Ocaña Lara, Francisco Antonio and Valderrama Bonnet, Mariano José},
}