Multivariate outlier detection in exploration geochemistry

By Peter Filzmoser, Robert G. Garrett, Clemens Reimann. Computational Geosciences, 2005

458 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cbe7c84467697f6dedabf9f3abd73d18c80d4bbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Karel Hron
2. Peter Filzmoser
3. Ricardo A. Maronna
4. Mark Werner
5. Davis J. McCarthy
6. John C. Marioni
7. Sarah A. Teichmann
8. Matthias Templ

In Statistics and Probability

1. Peter Filzmoser
2. Karel Hron
3. Mark Werner
4. Ricardo A. Maronna
5. Davis J. McCarthy
6. John C. Marioni
7. Matthias Templ
8. Patrick L. Combettes

Citations in the corpus

• Outlier Detection for Compositional Data Using Robust Methods (2008, background): “The multivariate outliers marked with “+” are in the northern costal area and around Monchegorsk, a big industrial center, and Apatity in the east (compare Filzmoser, Garrett, and Reimann, 2005).”
• Outlier identification in high dimensions (2008, methodology): “Many papers have been devoted to this data set, a part of the data was also used for multivariate outlier detection by Filzmoser et al. (2005).”
• Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “Instead of arbitrarily choosing a cutoff point and deciding whether a cell is of low or of high quality, we applied a widely used outlier detection algorithm to classify each cell (‘mvoutlier’ R package [37]).”
• Identification of Multivariate Outliers: A Performance Study (2016, methodology): “The critical values needed for the methods FGR and BG result from simulations with 1000 replications for the correspondingn and p and for the parameters (δ, ε; α) being used.”
• Principal Component Analysis on Spatial Data: An Overview (2013, unknown): “These same disciplines are also at the forefront in the application of robust forms of PCA and outlier detection (Filzmoser 1999 ; Filzmoser, Garrett, and Reimann 2005 ; Filzmoser, Hron, and Reiman 2009 ).”
• A MULTIVARIATE OUTLIER DETECTION METHOD (2004, background): “…value for distinguishing between outliers and extremes can be derived by simulation, and the result is approximately pcrit(δ, n, p) = 0.24− 0.003 · p√ n for δ = χ2p,0.975 and p ≤ 10 and pcrit(δ, n, p) = 0.252− 0.0018 · p√ n for δ = χ2p,0.975 and p > 10 (see Filzmoser, Reimann, and Garrett, 2003).”