21 citations · 28 across the 4 of their papers we have counts for
5 papers
Asgl: A Python Package for Penalized Linear and Quantile Regression
Ãlvaro 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…
Adaptive sparse group LASSO in quantile regression
Ãlvaro Méndez Civieta, M. Carmen Aguilera-Morillo, Rosa E. Lillo
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 th…
Variable Domain Multivariate Functional Principal Component Analysis
Pavel Hernández Amaro, MarÃa 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…