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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley

They define outlier detection as the problem of “[] finding patterns in data that do not conform to expected normal behavior“. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download. Robust PCA will be used to detect the atypical genomic markers, which have been . Tuesday, 9 April 2013 at 13:07. A different type of approach is to formulate the detection of differential splicing as an outlier detection problem, as in REAP (Regression-based Exon Array Protocol) or FIRMA (Finding Isoforms using Robust Multichip Analysis) [15,16]. Another useful survey article is “Robust statistics for outlier detection,” by Peter Rousseeuw and Mia Hubert. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. After an For example: neural networks, SVM, rule-based, clustering, nearest neighbors, regression, etc. Structure Discovery in Nonparametric Regression through Compositional Kernel Search - implementation - · Nuit Blanche in Review (May 2013) · How does the Rice one pixel camera work ? The implemented statistical method will be based on robust PCA [3], which decomposes a given data matrix into a low-rank component and a sparse component containing the outlier elements. The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar.