Paula Saavedra-Nieves
University of Santiago de Compostela
23 Papers
21 Citations
Paula Saavedra-Nieves is an academic researcher from University of Santiago de Compostela. The author has contributed to research in topics: Estimator & Level set. The author has an hindex of 5, co-authored 17 publications.
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Papers
Serum exosome inflamma‐miRs are surrogate biomarkers for asthma phenotype and severity
S. Vázquez-Mera,L. Martelo-Vidal,P. Miguéns-Suárez,Paula Saavedra-Nieves,Pilar Arias,Coral González-Fernández,Mar Mosteiro-Añón,María Dolores Corbacho-Abelaira,Marina Blanco-Aparicio,P Méndez-Brea,Francisco J. Salgado,Juan-José Nieto-Fontarigo,Francisco-Javier González-Barcala +12 more
TL;DR: Immune-related miRNAs, includingmiR-21-5p, miR-126-3p,MiR-146a- 5p, and miR -215-5P can be used as clinically relevant non-invasive biomarkers of the phenotype/endotype and severity of asthma.
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A fully data-driven method for estimating the shape of a point cloud
TL;DR: In this article, a new data-driven method for estimating the probability support of a random sample of points from some unknown distribution is proposed, which is able to achieve the same convergence rate as the convex hull for estimating convex sets, but under a much more exible smoothness shape condition.
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On systems of quotas from bankruptcy perspective: the sampling estimation of the random arrival rule
TL;DR: A sampling procedure for estimating the random arrival rule in bankruptcy situations is addressed, based on simple random sampling with replacement and an estimation method of the Shapley value for transferable utility games, especially useful when dealing with large-scale problems.
14
Nonparametric estimation of directional highest density regions
TL;DR: In this article, a plug-in estimator based on kernel smoothing and associated confidence regions is proposed for directional data, which minimizes the Hausdorff distance between the boundaries of the theoretical and estimated HDRs.
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Nonparametric estimation of directional highest density regions
TL;DR: In this paper, the authors define highest density regions for directional data and provide a plug-in estimator, based on kernel smoothing, which is applied to analyze two real data sets in animal orientation and seismology.
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