Unmasking Multiple Outliers in Multivariate Data
نویسندگان
چکیده
منابع مشابه
Z-Glyph: Visualizing outliers in multivariate data
Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, ‘‘Z-Glyph,’’ a ...
متن کاملPropagation of Outliers in Multivariate Data
We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that we call “propagation of outliers.” This source of error is unusual in the sense that it is genera...
متن کاملInterpretation of multivariate outliers for compositional data
data Peter Filzmoser, Karel Hron, Clemens Reimann Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstraße 8-10, A-1040 Vienna, Austria. Tel +43 1 58801 10733, FAX +43 1 58801 10799 Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Faculty of Science, 17. listopadu 12, CZ-77146 Olomouc, Czech Republic Geological...
متن کاملDetecting outliers in multivariate data while controlling false alarm rate
Outlier identification often implies inspecting each z-transformed variable and adding a Mahalanobis D2. Multiple outliers may mask each other by increasing variance estimates. Caroni & Prescott (1992) proposed a multivariate extension of Rosner’s (1983) technique to circumvent masking, taking sample size into account to keep the false alarm risk below, say, α = .05. Simulations studies here co...
متن کاملFinding multivariate outliers in fMRI time-series data
A fundamental challenge for researchers studying the brain is to explain how distributed patterns of brain activity relate to a specific representation or computation. Multivariate techniques are therefore becoming increasingly popular for pattern localization of functional magnetic resonance imaging (fMRI) data. The increased power of these techniques can be offset by their susceptibility to m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications for Statistical Applications and Methods
سال: 2006
ISSN: 2287-7843
DOI: 10.5351/ckss.2006.13.1.029