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Robust identification of target genes and outliers in triple-negative breast cancer data KU Leuven
Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that ...
MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers KU Leuven
© 2019, © 2019 American Statistical Association and the American Society for Quality. Multivariate data are typically represented by a rectangular matrix (table) in which the rows are the objects (cases) and the columns are the variables (measurements). When there are many variables one often reduces the dimension by principal component analysis (PCA), which in its basic form is not robust to outliers. Much research has focused on handling ...
Fast methods for analyzing large datasets with outliers KU Leuven
Robust statistics is an important tool in present-day data analysis, as datasetscommonly contain outliers. Due to the exceptional growth of the availabilityand size of datasets over the last two decades, computational cost plays an
increasingly important role in the utility of statistical procedures. Many ofthe original robust methods, developed in the 20th century, require substantialcomputation time. In this thesis, we propose three new ...
increasingly important role in the utility of statistical procedures. Many ofthe original robust methods, developed in the 20th century, require substantialcomputation time. In this thesis, we propose three new ...
Robust monitoring of time series with application to fraud detection KU Leuven
© 2018 The Author(s) Time series often contain outliers and level shifts or structural changes. These unexpected events are of the utmost importance in fraud detection, as they may pinpoint suspicious transactions. The presence of such unusual events can easily mislead conventional time series analysis and yield erroneous conclusions. A unified framework is provided for detecting outliers and level shifts in short time series that may have a ...