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Chapter and Conference Paper
Robust PCA for High-dimensional Data
Principal component analysis (PCA) is a well-known technique for dimension reduction. Classical PCA is based on the empirical mean and covariance matrix of the data, and hence is strongly affected by outlying ...
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Chapter and Conference Paper
A Robust Hotelling Test
Hotelling’s T2 statistic is an important tool for inference about the center of a multivariate normal population. However, hypothesis tests and confidence intervals based on this statistic can be adversely affect...
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Chapter and Conference Paper
A Hotelling Test Based on MCD
Hypothesis tests and confidence intervals based on the classical Hotelling T 2 statistic can be adversely affected by outliers. Therefore, we construct an alternative inference technique based on a ...
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Chapter and Conference Paper
A robust version of principal factor analysis
Our aim is to construct a factor analysis method that can resist the effect of outliers. We start with a highly robust initial covariance estimator, after which the factors can be obtained from maximum likelih...
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Article
Depth in an Arrangement of Hyperplanes
A collection of n hyperplanes in \({\Bbb R}\) d forms a hyperplane arrangement. The depth of a point $\theta \in ...
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Chapter
R
Regular Ring - A regular ring (in the sense of von Neumann) admitting an involutory anti-automorphism α→α* such that αα =0 implies α=0. An idempotent e of a *-regular ring is called a projec...
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Chapter and Conference Paper
Some Proposals for Fast HBD Regression
Existing high-breakdown regression estimators need substantial computation time. In this paper we propose a fast estimator for robust regression with a breakdown point of 1/3. This is not the highest value pos...