Functional Boxplots

Sun, Ying; Genton, Marc G.

doi:10.1198/jcgs.2011.09224

Cited by 424 publications

(429 citation statements)

References 15 publications

Supporting

Mentioning

425

Contrasting

Unclassified

Order By: Relevance

“…This section presents a method for the visualization and analysis of functional data developed by Sun and Genton 7) . This method is based on the notion of depth band that classifies a sample of curves.…”

Section: Methods Using Band Depth Conceptmentioning

confidence: 99%

“…If the highest band depth is reached by two different curves, the most central curve is the average of these curves where the maximal value is reached. Figure 10 shows a sample from Sun and Genton 7) to illustrate this concept. The sample is composed of 4 curves, from which we can therefore form 6 bands of 2 curves.…”

Section: Band Depth Conceptmentioning

confidence: 99%

“…Moreover, within this zone, the black curve represents the central (or median) curve. Sun and Genton 7) propose to consider as outlier any curve that is not completely within a region (not shown) obtained by "increasing" the central region, down and up, by an amount at each point proportional to the height of the band. The proportionality factor (sometimes called "expansion factor" in the following) is equal to 1.5 by default, by analogy with the usual boxplot.…”

Section: Functionnal Boxplot Based On Band Depthmentioning

confidence: 99%

“…The first part of this article presents two methods introduced by Hyndman and Shang 6) , based on dimension reduction. The second part is dedicated to the functional boxplot of Sun and Genton 7) , which implies depth of band concept.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients

Popelin

Iooss

2014

SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo

View full text Add to dashboard Cite

In nuclear engineering studies, uncertainty and sensitivity analyses of simulation computer codes can be faced to the complexity of the input and/or the output variables. If these variables represent a transient or a spatial phenomenon, the difficulty is to provide tool adapted to their functional nature. In this paper, we describe useful visualization tools in the context of uncertainty analysis of model transient outputs. Our application involves thermal-hydraulic computations for safety studies of nuclear pressurized water reactors.

show abstract

Section: Methods Using Band Depth Conceptmentioning

confidence: 99%

Section: Band Depth Conceptmentioning

confidence: 99%

Section: Functionnal Boxplot Based On Band Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients

Popelin

Iooss

2014

SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo

View full text Add to dashboard Cite

show abstract

“…The construction of the boxplot for functional data has been proposed quite recently 9 . The functional boxplot provides the same characteristics as the classical boxplot.…”

Section: Boxplot For Functional Datamentioning

confidence: 99%

Analysis of the seasonal incidence of acute respiratory infections including influenza (ARI) in the Czech Republic - possible contribution of the functional data boxplot in epidemiology

Vencálek¹,

Kynčl²

2017

Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub

View full text Add to dashboard Cite

Aims. The detection of an epidemic outbreak is possible only if the baseline incidence level of a given disease is well defined. The determination of the baseline is complicated by the presence of epidemic outbreaks in historical data. The aim of the paper is to provide a new way of determining the baseline. Methods. The analyzed data containing weekly records on the incidence of acute respiratory infections including influenza (ARI) in the Czech Republic and its regions are taken from the nationwide surveillance system; data on 15 seasons from 2001/02 to 2015/16 are included. Functional boxplots of the data are constructed and five distinct methods (componentwise mean, componentwise median, median, trimmed mean, and adjusted mean) were used for the computation of the baseline level function. Results. It was shown that the methods based on functional data analysis could successfully overcome the problems that arise when the conventional methods are used for the determination of the baseline function. Conclusion. The functional boxplot -a new statistical tool -can bring not only a transparent visualisation of comprehensive data, but can also help epidemiologists and other public health experts to determine the baseline incidence level of a given disease as well as to detect unusual epidemic seasons.

show abstract

Outliers

Aelst¹

2012

Encyclopedia of Environmetrics

View full text Add to dashboard Cite

Datasets frequently contain outliers which are observations that strongly deviate from the pattern in the majority of the data. Detecting outliers and measuring their influence on an estimation procedure is not trivial. In one or two dimensions graphical inspection of the data may be helpful, but in higher dimensions more reliable methods are needed. We discuss robust estimation to obtain estimates that can resist the influence of outliers and related outlier diagnostics to identify the outliers in a dataset.

show abstract

Functional Boxplots

Cited by 424 publications

References 15 publications

Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients

Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients

Analysis of the seasonal incidence of acute respiratory infections including influenza (ARI) in the Czech Republic - possible contribution of the functional data boxplot in epidemiology

Outliers

Contact Info

Product

Resources

About