Nora M. Villanueva

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Verified

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• Doctor of Philosophy
• Professor (Assistant) at University of Vigo

User and Entity Behaviour Analytics (UEBA) is a broad branch of data analytics that attempts to build a normal behavioural profile in order to detect anomalous events. Among the techniques used to detect anomalies, Deep Autoencoders constitute one of the most promising deep learning models on UEBA tasks, allowing explainable detection of security i...

The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the e...

Neural Networks are one of the most popular methods nowadays given their high performance on diverse tasks, such as computer vision, anomaly detection, computer-aided disease detection and diagnosis or natural language processing. While neural networks are known for their high performance, they often suffer from the so-called “black-box” problem, w...

Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composition and skewness, but most of them lack any measurement of statistical support or were not develope...

In many situations, it could be interesting to ascertain whether groups of curves can be performed, especially when confronted with a considerable number of curves. This paper introduces an R package, known as clustcurv, for determining clusters of curves with an automatic selection of their number. The package can be used for determining groups in...

In many situations it could be interesting to ascertain whether nonparametric regression curves can be grouped, especially when confronted with a considerable number of curves. The proposed testing procedure allows to determine groups with an automatic selection of their number. A simulation study is presented in order to investigate the finite sam...

Survival analysis includes a wide variety of methods for analyzing time‐to‐event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of eq...

Survival analysis includes a wide variety of methods for analyzing time-to-event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for cen-sored data. When the null hypothesis of e...

We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve interactions. The main feature of the package is its ability to perform inference regarding these models. Namely, the implementation of...

In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDse...

A major obstacle hampering the therapeutic application of regulatory T (Treg) cells is the lack of suitable extracellular markers, which complicates their identification/isolation. Treg cells are normally isolated via CD25 (IL-2Rα) targeting, but this protein is also expressed by activated CD4(+) effector T (Teff) lymphocytes. Other extracellular (...

A question that tends to arise in multiple regression models (with p variables), and that has not been totally satisfactory solved, is to determine the best subset or subsets of q q≤p) predictors which will establish the model or models with the best discrimination capacity. This problem is particularly important where p is high and/or where the...

Analysis of such studies can be successfully performed using nonparametric regression models. In the nonparametric regression framework, issues of interest include the so-called factor-by-curve interaction, where the effect of a continuous covariate on response varies across groups defined by levels of a categorical variable. This study sought to c...

In the nonparametric regression framework, issues of interest include the so-called factor-by-curve interaction, where the effect of a continuous co-variate on response varies across groups defined by levels of a categorical variable. This study introduces a software application for R which per-forms inference in a nonparametric regression model. I...

In multiple regression models with p variables is important to determine the best subset or subsets of q (q ≤ p) predictors which will establish the model or models with the best discrimination capacity. This problem is particularly important where p is high and/or where there are mutu-ally redundant predictors. With this study, we present a new ap...