Robust bivariate boxplots and multiple outlier detection

Zani, Sergio; Riani, Marco; Corbellini, Aldo

doi:10.1016/s0167-9473(98)00040-1

Cited by 84 publications

(39 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Usually m 0 = N + 1 or slightly larger. A possible alternative for the selection of the initial subset could be the robust bivariate boxplot introduced by Zani et al (1998). The basic idea of the forward search is then to fit subsets of increasing size m, with m 0 ≤ m ≤ T , in such a way that outliers and other observations not following the general structure of the data are clearly pointed out by diagnostic monitoring.…”

Section: Preliminaries and Backgroundmentioning

confidence: 99%

Robust estimation of efficient mean–variance frontiers

Grossi¹,

Laurini²

2011

Adv Data Anal Classif

View full text Add to dashboard Cite

show abstract

Section: Preliminaries and Backgroundmentioning

confidence: 99%

Robust estimation of efficient mean–variance frontiers

Grossi¹,

Laurini²

2011

Adv Data Anal Classif

View full text Add to dashboard Cite

show abstract

“…However, a more accurate detection of outliers is likely to be achieved by using several variables. For this purpose, there are bivariate extensions of the boxplot (GOLDBERG and IGLEWlCZ, 1992;ZANI et al, 1998) or heuristic methods (ATKINSON, 2001;ROUSSEEUW andDRIESSEN, 1999, HYNDMAN, 1996), but in most cases they are computationally cumbersome or rely on visual detection. However, the principles of the bivariate quelplot proposed by Goldberg and Iglewicz axe attractive, because their outlier detection uses analytic expressions and allows differing spread in the different directions of the data.…”

Section: Outlier Detectionmentioning

confidence: 99%

On-line signal quality estimation of multichannel surface electromyograms

Grönlund

Roeleveld

Holtermann

et al. 2005

Med. Biol. Eng. Comput.

View full text Add to dashboard Cite

When multichannel surface-electromyography (MCSEMG) systems are used, there is a risk of recording low-quality signals. Such signals can be confusing for analysis and interpretation and can be caused by power-line interference, motion artifacts or poor electrode-skin contact. Usually, the electrode-skin impedance is measured to estimate the quality of the contact between the electrodes and the skin. However, this is not always practical, and the contact can change over short time-scales. A fast method is described to estimate the quality of individual signals of monopolar MCSEMG recordings based on volume conduction of myo-electric signals. The characteristics of the signals were described using two descriptor variables. Outliers (extreme data points) were detected in the two-dimensional distributions of the descriptor variables using a non-parametric technique, and the quality of the signals was estimated by their outlier probabilities. The method's performance was evaluated using 1 s long signals visually classified as very poor (G 1), poor (G2) or good quality (G3). Recordings from different subjects, contraction levels and muscles were used. An optimum threshold at 0.05 outlier probability was proposed and resulted in classification accuracies of 100% and > 70% for G I and G2 signals, respectively, whereas <5% of the G3 signals were classified as poor. In conclusion, the proposed method estimated MCSEMG signal quality with high accuracy, compared with visual assessment, and is suitable for on-line implementation. The method could be applied to other multichannel sensor systems, with an arbitrary number of descriptor variables, when their distributions can be assumed to lie within a certain range.

show abstract

“…While small multiples have most often been used for display and analysis of multiple, univariate representations, the scatterplot matrix by design focuses on multiple, bivariate relationships. Many extensions have been made to the basic method over the years, see: (Becker & Cleveland, 1987), (Reed et al, 1995), (Rousseeuw et al, 1999;Zani et al, 1998). Several authors have used dynamic linking to combine the scatterplot matrix with other display forms such as maps (Carr et al, 1987;Cook et al, 1997;Monmonier, 1989) and alternative multivariate depictions (Schmid & Hinterberger, 1994).…”

Section: Linked Bivariate Matricesmentioning

confidence: 99%

Exploring high-D spaces with multiform matrices and small multiples

MacEachren

Xiping

Hardisty

et al.

IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714)

View full text Add to dashboard Cite

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.

show abstract

Robust bivariate boxplots and multiple outlier detection

Cited by 84 publications

References 16 publications

Robust estimation of efficient mean–variance frontiers

Robust estimation of efficient mean–variance frontiers

On-line signal quality estimation of multichannel surface electromyograms

Exploring high-D spaces with multiform matrices and small multiples

Contact Info

Product

Resources

About