Carlo Sguera
Charles III University of Madrid
17 Papers
54 Citations
Carlo Sguera is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Outlier & Anomaly detection. The author has an hindex of 3, co-authored 16 publications. Previous affiliations of Carlo Sguera include Complutense University of Madrid.
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Papers
Functional outlier detection by a local depth with application to NOx levels
TL;DR: In this paper, the authors proposed methods to detect outliers in functional data sets and the task of identifying atypical curves is carried out using the recently proposed kernelized functional spatial depth (KFSD).
Unsupervised Scalable Statistical Method for Identifying Influential Users in Online Social Networks.
Arturo Azcorra,Arturo Azcorra,Luis F. Chiroque,Luis F. Chiroque,Rubén Cuevas,A. Fernández Anta,Henry Laniado,Rosa E. Lillo,Juan Romo,Carlo Sguera +9 more
TL;DR: This report proposes a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users, which is scalable, and can hence be used in large OSNs.
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Detecting and Classifying Outliers in Big Functional Data
TL;DR: Two new outlier detection methods, which are useful for identifying different types of outliers in (big) functional data sets, are proposed, and a fast implementation which uses the component-wise median in the computation of the indices is presented.
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A notion of depth for sparse functional data
Carlo Sguera,Sara López-Pintado +1 more
TL;DR: In this paper, the authors proposed a new method that allows the curve estimation uncertainty to be incorporated into the depth analysis by using both functional estimates and their associated confidence intervals, and they described the new approach using the modified band depth although any other functional depth could be used.
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A notion of depth for sparse functional data
Carlo Sguera,Sara López-Pintado +1 more
TL;DR: This work proposes a new method that allows the curve estimation uncertainty to be incorporated into the depth analysis, and describes the new approach using the modified band depth although any other functional depth could be used.
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