The Experts below are selected from a list of 72828 Experts worldwide ranked by ideXlab platform
Marc G. Genton - One of the best experts on this subject based on the ideXlab platform.
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Trajectory Functional Boxplots
Stat, 2020Co-Authors: Zonghui Yao, Wenlin Dai, Marc G. GentonAbstract:We thank Dr. Donald H. House and his group at Clemson University for sharing the ensemble hurricane generator code. The research reported in this paper was supported by King Abdullah University of Science and Technology (KAUST).
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Trajectory Functional Boxplots
arXiv: Methodology, 2019Co-Authors: Zonghui Yao, Wenlin Dai, Marc G. GentonAbstract:With the development of data-monitoring techniques in various fields of science, multivariate functional data are often observed. Consequently, an increasing number of methods have appeared to extend the general summary statistics of multivariate functional data. However, trajectory functional data, as an important sub-type, have not been studied very well. This article proposes two informative exploratory tools, the trajectory functional boxplot, and the modified simplicial band depth (MSBD) versus Wiggliness of Directional Outlyingness (WO) plot, to visualize the centrality of trajectory functional data. The newly defined WO index effectively measures the shape variation of curves and hence serves as a detector for shape outliers; additionally, MSBD provides a center-outward ranking result and works as a detector for magnitude outliers. Using the two measures, the functional boxplot of the trajectory reveals center-outward patterns and potential outliers using the raw curves, whereas the MSBD-WO plot illustrates such patterns and outliers in a space spanned by MSBD and WO. The proposed methods are validated on hurricane path data and migration trace data recorded from two types of birds.
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Functional Boxplots for multivariate curves
Stat, 2018Co-Authors: Wenlin Dai, Marc G. GentonAbstract:This research was supported by the King Abdullah University of Science and Technology (KAUST).
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An exploratory data analysis of electroencephalograms using the functional Boxplots approach
Frontiers in neuroscience, 2015Co-Authors: Duy-tan Ngo, Marc G. Genton, Ying Ying Sun, Ramesh Srinivasan, Steven C. Cramer, Hernando OmbaoAbstract:Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEG) in order to understand electrical neural data. In this work, we propose to use the functional boxplot to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve -- which is not equivalent to connecting medians obtained from frequency-specific Boxplots. In addition, this approach identifies a functional median, summarizes variability and detects potential outliers. By extending functional Boxplots analysis from one-dimensional curves to surfaces, surface Boxplots are also used to explore the variation of the spectral power for the alpha (8-12 Hertz) and beta (16-32 Hertz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.
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Surface Boxplots: Surface Boxplots
Stat (International Statistical Institute), 2014Co-Authors: Marc G. Genton, Christopher R. Johnson, Kristin Potter, Georgiy StenchikovAbstract:In this paper, we introduce a surface boxplot as a tool for visualization and exploratory analysis of samples of images. First, we use the notion of volume depth to order the images viewed as surfaces. In particular, we define the median image. We use an exact and fast algorithm for the ranking of the images. This allows us to detect potential outlying images that often contain interesting features not present in most of the images. Second, we build a graphical tool to visualize the surface boxplot and its various characteristics. A graph and histogram of the volume depth values allow us to identify images of interest. The code is available in the supporting information of this paper. We apply our surface boxplot to a sample of brain images and to a sample of climate model outputs.
Robert M. Kirby - One of the best experts on this subject based on the ideXlab platform.
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Curve Boxplot: Generalization of Boxplot for Ensembles of Curves
IEEE transactions on visualization and computer graphics, 2014Co-Authors: Mahsa Mirzargar, Ross T. Whitaker, Robert M. KirbyAbstract:In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or Boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.
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Contour Boxplots: A Method for Characterizing Uncertainty in Feature Sets from Simulation Ensembles
IEEE transactions on visualization and computer graphics, 2013Co-Authors: Ross T. Whitaker, Mahsa Mirzargar, Robert M. KirbyAbstract:Ensembles of numerical simulations are used in a variety of applications, such as meteorology or computational solid mechanics, in order to quantify the uncertainty or possible error in a model or simulation. Deriving robust statistics and visualizing the variability of an ensemble is a challenging task and is usually accomplished through direct visualization of ensemble members or by providing aggregate representations such as an average or pointwise probabilities. In many cases, the interesting quantities in a simulation are not dense fields, but are sets of features that are often represented as thresholds on physical or derived quantities. In this paper, we introduce a generalization of Boxplots, called contour Boxplots, for visualization and exploration of ensembles of contours or level sets of functions. Conventional Boxplots have been widely used as an exploratory or communicative tool for data analysis, and they typically show the median, mean, confidence intervals, and outliers of a population. The proposed contour Boxplots are a generalization of functional Boxplots, which build on the notion of data depth. Data depth approximates the extent to which a particular sample is centrally located within its density function. This produces a center-outward ordering that gives rise to the statistical quantities that are essential to Boxplots. Here we present a generalization of functional data depth to contours and demonstrate methods for displaying the resulting Boxplots for two-dimensional simulation data in weather forecasting and computational fluid dynamics.
D. Shannon - One of the best experts on this subject based on the ideXlab platform.
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Box‐and‐whisker plots with the SAS System®
Pharmaceutical Statistics, 2003Co-Authors: D. ShannonAbstract:With the introduction of Proc BOXPLOT to the SAS/STAT module statisticians now have the power to produce several styles of box-and-whisker plots, thus enabling comparative displays of data groups to be easily presented. This paper examines the statistical capabilities of Proc BOXPLOT, the styles of box-and-whisker plot it can produced and points out the pitfalls those programming the procedure should consider.
Antonio Balzanella - One of the best experts on this subject based on the ideXlab platform.
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ECDA - On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series
Data Science Learning by Latent Structures and Knowledge Discovery, 2015Co-Authors: Elvira Romano, Antonio BalzanellaAbstract:In this paper we introduce a micro-clustering strategy for functional Boxplots. The aim is to summarize a set of streaming time series split in non-overlapping windows. It is a two-step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-clusters; then, it reveals the final summarization by processing, off-line, the functional data structures. Our main contribute consists in providing a new definition of micro-cluster based on Functional Boxplots and in defining a proximity measure which allows to compare and update them. This allows to get a finer graphical summarization of the streaming time series by five functional basic statistics of data. The obtained synthesis will be able to keep track of the dynamic evolution of the multiple streams.
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on line clustering of functional Boxplots for monitoring multiple streaming time series
ECDA, 2015Co-Authors: Elvira Romano, Antonio BalzanellaAbstract:In this paper we introduce a micro-clustering strategy for functional Boxplots. The aim is to summarize a set of streaming time series split in non-overlapping windows. It is a two-step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-clusters; then, it reveals the final summarization by processing, off-line, the functional data structures. Our main contribute consists in providing a new definition of micro-cluster based on Functional Boxplots and in defining a proximity measure which allows to compare and update them. This allows to get a finer graphical summarization of the streaming time series by five functional basic statistics of data. The obtained synthesis will be able to keep track of the dynamic evolution of the multiple streams.
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Clustering of functional Boxplots for multiple streaming time series
arXiv: Methodology, 2012Co-Authors: Elvira Romano, Antonio BalzanellaAbstract:In this paper we introduce a micro-clustering strategy for Functional Boxplots. The aim is to summarize a set of streaming time series splitted in non overlapping windows. It is a two step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-clusters; then it reveals the final summarization by processing, off-line, the functional data structures. Our main contribute consists in providing a new definition of micro-cluster based on Functional Boxplots and, in defining a proximity measure which allows to compare and update them. This allows to get a finer graphical summarization of the streaming time series by five functional basic statistics of data. The obtained synthesis will be able to keep track of the dynamic evolution of the multiple streams.
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functional Boxplots for summarizing and detecting changes in environmental data coming from sensors
Spatial2 Conference: Spatial Data Methods for Environmental and Ecological Processes Foggia (IT) 1-2 September 2011, 2011Co-Authors: Elvira Romano, Antonio Balzanella, Lidia RivoliAbstract:Nowadays, environmental sensor networks produce a large amount of streaming time series whose storage, manipulation and indexing is impractical. In this work, we propose a new strategy for summarizing and describing this kind of data based on functional data representation. It discovers trends and potential anomalies by using an informative exploratory tool: the functional boxplot. Functional Boxplots are introduced for conveying location and variability information. In addition, for detecting and illustrating variation a distance among functional Boxplots is used.
Ellen Vandervieren - One of the best experts on this subject based on the ideXlab platform.
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An adjusted boxplot for skewed distributions
Computational Statistics & Data Analysis, 2008Co-Authors: Mia Hubert, Ellen VandervierenAbstract:The boxplot is a very popular graphical tool for visualizing the distribution of continuous unimodal data. It shows information about the location, spread, skewness as well as the tails of the data. However, when the data are skewed, usually many points exceed the whiskers and are often erroneously declared as outliers. An adjustment of the boxplot is presented that includes a robust measure of skewness in the determination of the whiskers. This results in a more accurate representation of the data and of possible outliers. Consequently, this adjusted boxplot can also be used as a fast and automatic outlier detection tool without making any parametric assumption about the distribution of the bulk of the data. Several examples and simulation results show the advantages of this new procedure.