Marta Sestelo

Universidade de Vigo

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i-10 index

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Co-Authors

Nora M. Villanueva

Universidade de Vigo

Javier Roca‐Pardiñas

Universidade de Vigo

Luís Meira‐Machado

University of Minho

Ines Ortega-Fernandez

Centro Tecnolóxico de Telecomunicacións de Galicia

Gorka Bidegain

University of the Basque Country

ÁG

Ángel Guerra

Hospital Intermutual de Levante

Manuel E. Garcí

Institute of Marine Research

Marcos Regueira

Instituto de Investigacións Mariñas

Jorge Hernández‐Urcera

Instituto de Investigacións Mariñas

Celestino Ordóñez

Universidad de Oviedo

Cooperative Institutions

Consejo Superior de Investigaciones Científicas

Universidade de Vigo

Centro Tecnolóxico de Telecomunicacións de Galicia

Instituto de Investigacións Mariñas

Institute of Marine Research

University of Southern Mississippi

University of Minho

Instituto Español de Oceanografía

University of the Basque Country

Universidad de Cantabria

Author Statistics

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Citation

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An automatic procedure to determine groups of nonparametric regression curves

arXiv (Cornell University) (2020)

Nora M. Villanueva Marta Sestelo Celestino Ordóñez Javier Roca‐Pardiñas

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 sample properties of the proposed methods when compared to existing alternative procedures. Finally, the applicability of the procedure to study the geometry of a tunnel by analysing a set of cross-sections is demonstrated. The results obtained show the existence of some heterogeneity in the tunnel geometry.

Sample (material)

10.48550/arxiv.2012.15278

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An R package for determining groups in multiple survival curves

Nora M. Villanueva Marta Sestelo Luís Meira‐Machado

R package

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Predicting SO2 pollution incidents by means of additive models with optimum variable selection

Atmospheric Environment (2014)

Marta Sestelo Javier Roca‐Pardiñas Celestino Ordóñez

10.1016/j.atmosenv.2014.06.025

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A New Approach for Dynamic and Risk-Based Data Anonymization.

Lilian Adkinson-Orellana Pablo Dago Casas Marta Sestelo Borja Pintos Castro

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survidm: An R package for Inference and Prediction in an Illness-Death Model

The R Journal (2021)

Gustavo Soutinho Marta Sestelo Luís Meira‐Machado

Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time.The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate "diseased" state before entering into a terminal absorbing state.In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution.However, biomedical researchers are also interested in reporting other interpretable results in a simple and summarized manner.These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions.The development of survidm package has been motivated by recent contribution that provides answers to all these topics.An illustration of the software usage is included using real data.

R package

10.32614/rj-2021-070

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Citations (6)

Selecting Variables in Regression Models [R package FWDselect version 2.1.0]

Marta Sestelo Nora M. Villanueva Javier Roca‐Pardiñas

R package

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Explainable generalized additive neural networks with independent neural network training

Statistics and Computing (2023)

Ines Ortega-Fernandez Marta Sestelo Nora M. Villanueva

Abstract 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, which means that it is difficult to understand how the model makes decisions. We introduce a neural network topology based on Generalized Additive Models. By training an independent neural network to estimate the contribution of each feature to the output variable, we obtain a highly accurate and explainable deep learning model, providing a flexible framework for training Generalized Additive Neural Networks which does not impose any restriction on the neural network architecture. The proposed algorithm is evaluated through different simulation studies with synthetic datasets, as well as a real-world use case of Distributed Denial of Service cyberattack detection on an Industrial Control System. The results show that our proposal outperforms other GAM-based neural network implementations while providing higher interpretability, making it a promising approach for high-risk AI applications where transparency and accountability are crucial.

Interpretability

Feature (linguistics)

10.1007/s11222-023-10320-5

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Citations (4)

Nonparametric Estimation of Regression Models withFactor-by-Curve Interactions

Marta Sestelo Nora M. Villanueva Javier Roca‐Pardiñas

Semiparametric regression

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Correction to: Explainable generalized additive neural networks with independent neural network training

Statistics and Computing (2024)

Ines Ortega-Fernandez Marta Sestelo Nora M. Villanueva

Training set

10.1007/s11222-024-10461-1

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seq2R: An R Package to Detect Change Points in DNA Sequences

Mathematics (2023)

Nora M. Villanueva Marta Sestelo Miguel M. Fonseca Javier Roca‐Pardiñas

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 developed taking into account the specificities of mitochondrial genomes. A new methodology is presented, which is specifically developed for mtDNA to detect compositional changes or asymmetries (AT and CG skews) based on nonparametric regression models and their derivatives. The proposed method also includes the construction of confidence intervals, which are built using bootstrap techniques. This paper introduces an R package, known as seq2R, that implements the proposed methodology. Moreover, an illustration of the use of seq2R is provided using real data, specifically two publicly available complete mtDNAs: the human (Homo sapiens) sequence and a nematode (Radopholus similis) mitogenome sequence.

Homo sapiens

Sequence (biology)

R package

10.3390/math11102299

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