12 citations · 20 across the 6 of their papers we have counts for
6 papers
Variable Domain Multivariate Functional Principal Component Analysis
Pavel Hernández Amaro, María Durbán, M. Carmen Aguilera-Morillo +3
Multivariate functional principal component analysis (MFPCA) is a powerful dimension reduction technique for analyzing multiple functional variables simultaneously. However, existi…
A novel generalized additive scalar-on-function regression model for partially observed multidimensional functional data: An application to air quality classification
Pavel Hernández-Amaro, Maria Durban, M. Carmen Aguilera-Morillo
In this work we propose a generalized additive functional regression model for partially observed functional data. Our approach accommodates functional predictors of varying dimens…
Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS
M Carmen Aguilera-Morillo, Ana M Aguilera
A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed.…
Modelling physical activity profiles in COPD patients: a fully functional approach to variable domain functional regression models
Pavel Hernandez-Amaro, Maria Durban, M. Carmen Aguilera-Morillo +2
Physical activity plays a significant role in the well-being of individuals with Chronic obstructive Pulmonary Disease (COPD). Specifically, it has been directly associated with ch…
Asgl: A Python Package for Penalized Linear and Quantile Regression
Álvaro Méndez Civieta, M. Carmen Aguilera-Morillo, Rosa E. Lillo
asgl is an open-source Python package that offers a robust and versatile framework for fitting a variety of regression models including linear, logistic, and, notably, quantile reg…
Fast Partial Quantile Regression
Alvaro Mendez Civieta, M. Carmen Aguilera-Morillo, Rosa E. Lillo
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 dimensio…