96 citations · 374 across the 25 of their papers we have counts for
27 papers
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials
Pablo Morala, Jenny Alexandra Cifuentes, Rosa E. Lillo +1
The nn2poly package provides the implementation in R of the NN2Poly method to explain and interpret feed-forward neural networks by means of polynomial representations that predict…
Comparing Lasso and Adaptive Lasso in High-Dimensional Data: A Genetic Survival Analysis in Triple-Negative Breast Cancer
Pilar González-Barquero, Rosa E. Lillo, Álvaro Méndez-Civieta
In high-dimensional survival analysis, effective variable selection is crucial for both model interpretation and predictive performance. This paper investigates Cox regression with…
Analysis of an aggregate loss model in a Markov renewal regime
Pepa Ramírez-Cobo, Emilio Carrizosa, Rosa Elvira Lillo
In this article we consider an aggregate loss model with dependent losses. The losses occurrence process is governed by a two-state Markovian arrival process (MAP2), a Markov renew…
A bivariate two-state Markov modulated Poisson process for failure modelling
Yoel G. Yera, Rosa E. Lillo, Bo F. Nielsen +2
Motivated by a real failure dataset in a two-dimensional context, this paper presents an extension of the Markov modulated Poisson process (MMPP) to two dimensions. The one-dimensi…
Fitting procedure for the two-state Batch Markov modulated Poisson process
Yoel G. Yera, Rosa E. Lillo, Pepa Ramírez-Cobo
The Batch Markov Modulated Poisson Process (BMMPP) is a subclass of the versatile Batch Markovian Arrival process (BMAP) which has been proposed for the modeling of dependent event…
Clustering multivariate functional data using the epigraph and hypograph indices: a case study on Madrid air quality
Belén Pulido, Alba M. Franco-Pereira, Rosa E. Lillo
With the rapid growth of data generation, advancements in functional data analysis (FDA) have become essential, especially for approaches that handle multiple variables at the same…
Multivariate Functional Outlier Detection using the FastMUOD Indices
Oluwasegun Taiwo Ojo, Antonio Fernández Anta, Marc G. Genton +1
We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects ou…
NN2Poly: A polynomial representation for deep feed-forward artificial neural networks
Pablo Morala, Jenny Alexandra Cifuentes, Rosa E. Lillo +1
Interpretability of neural networks and their underlying theoretical behavior remain an open field of study even after the great success of their practical applications, particular…
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…
Functional clustering via multivariate clustering
Belén Pulido, Alba María Franco-Pereira, Rosa Elvira Lillo
Clustering techniques applied to multivariate data are a very useful tool in Statistics and have been fully studied in the literature. Nevertheless, these clustering methodologies…
Towards a mathematical framework to inform Neural Network modelling via Polynomial Regression
Pablo Morala, Jenny Alexandra Cifuentes, Rosa E. Lillo +1
Even when neural networks are widely used in a large number of applications, they are still considered as black boxes and present some difficulties for dimensioning or evaluating t…
Automatic elimination of the pectoral muscle in mammograms based on anatomical features
Jairo A. Ayala-Godoy, Rosa E. Lillo, Juan Romo
Digital mammogram inspection is the most popular technique for early detection of abnormalities in human breast tissue. When mammograms are analyzed through a computational method,…
Detecting and Classifying Outliers in Big Functional Data
Oluwasegun Taiwo Ojo, Antonio Fernández Anta, Rosa E. Lillo +1
We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed methods are based on an existi…
Adaptive sparse group LASSO in quantile regression
Álvaro 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…
Robust regression based on shrinkage estimators
Elisa Cabana, Rosa E. Lillo, Henry Laniado
A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously stu…
Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators
Elisa Cabana, Rosa E. Lillo, Henry Laniado
A collection of robust Mahalanobis distances for multivariate outlier detection is proposed, based on the notion of shrinkage. Robust intensity and scaling factors are optimally es…
On the estimation of extreme directional multivariate quantiles
Raúl Torres, Elena Di Bernardino, Henry Laniado +1
In multivariate extreme value theory (MEVT), the focus is on analysis outside of the observable sampling zone, which implies that the region of interest is associated to high risk…
An empirical comparison of global and local functional depths
Carlo Sguera, Rosa E. Lillo
A functional data depth provides a center-outward ordering criterion which allows the definition of measures such as median, trimmed means, central regions or ranks in a functional…
Directional Multivariate Extremes in Environmental Phenomena
Raúl Torres, Carlo De Michele, Henry Laniado +1
Several environmental phenomena can be described by different correlated variables that must be considered jointly in order to be more representative of the nature of these phenome…
Homogeneity test for functional data
Ramón Flores, Rosa Lillo, Juan Romo
In the context of functional data analysis, we propose new two sample tests for homogeneity. Based on some well-known depth measures, we construct four different statistics in orde…
A Directional Multivariate Value at Risk
Raúl Torres, Rosa E. Lillo, Henry Laniado
In economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horiz…
Functional outlier detection by a local depth with application to NOx levels
Carlo Sguera, Pedro Galeano, Rosa Lillo
This paper proposes methods to detect outliers in functional data sets and the task of identifying atypical curves is carried out using the recently proposed kernelized functional…
Extremality measures and a rank test for functional data
A. M. Franco-Pereira, R. E. Lillo, J. Romo
The statistical analysis of functional data is a growing need in many research areas. In particular, a robust methodology is important to study curves, which are the output of expe…