Mendez-Civieta, Alvaro; Aguilera-Morillo, M. Carmen; Lillo, Rosa E.(Springer-Verlag, 2021-09)
[EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage ...
Moreno-Oyervides, Aldo; Martín-Mateos, Pedro; Aguilera-Morillo, M. Carmen; Ulisse, Giacomo; Arriba, María C.; Durban, María; Del Rio, Marcela; Larcher, Fernando; Krozer, Viktor; Acedo, Pablo(MDPI AG, 2019-08-01)
[EN] Diabetes is a very complex condition affecting millions of people around the world. Its occurrence, always accompanied by sustained hyperglycemia, leads to many medical complications that can be greatly mitigated when ...
Méndez-Civieta, Álvaro; Aguilera-Morillo, M. Carmen; Lillo, Rosa E.(Elsevier, 2022-04-15)
[EN] Partial least squares (PLS) is a dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in situations where the data is colinear or high dimensional. Both PLS and OLS provide mean ...
Laria, Juan C.; Aguilera-Morillo, M. Carmen; Lillo, Rosa E.(Springer-Verlag, 2022-02)
[EN] This paper introduces the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse Group Lasso regularization to calculate ...
Ruiz-Castro, Juan E.; Acal, Christian; Aguilera, Ana M.; Aguilera-Morillo, M. Carmen; Roldán, Juan B.(Elsevier, 2021-08)
[EN] Functional principal component analysis (FPCA) based on Karhunen-Loeve (K-L) expansion allows to describe the stochastic evolution of the main characteristics associated to multiple systems and devices. Identifying ...