[Statlist] Next talk: Friday, December 02, 2011 with Genton Marc G., Texas A&M University

[Cecilia Rey]

ETH and University of Zurich

Proff. P. Buehlmann -  L. Held - H.R. Kuensch -
M. Maathuis -  S. van de Geer - M. Wolf


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We are glad to announce the following talk
Friday, December 02, 2011, 15.15h, HG G 19.1

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by Marc G. Genton, Texas A&M University, Department of Statistics


Title:
Functional Boxplots for Visualization of Complex Curve/Image Data: An  
Application to Precipitation and Climate Model Output

Abstract:
In many statistical experiments, the observations are functions by  
nature, such as temporal curves or spatial surfaces/images, where the  
basic unit of information is the entire observed function rather than  
a string of numbers. For example the temporal evolution of several  
cells, the intensity of medical images of the brain from MRI, the  
spatio-temporal records of precipitation in the U.S., or the output  
from climate models, are such complex data structures. Our interest  
lies in the visualization of such data and the detection of outliers.  
With this goal in mind, we have defined functional boxplots and  
surface boxplots. Based on the center outwards ordering induced by  
band depth for functional data or surface data, the descriptive  
statistics of such boxplots are: the envelope of the 50% central  
region, the median curve/image and the maximum non-outlying envelope.  
In addition, outliers can be detected in a functional/surface boxplot  
by the 1.5 times the 50% central region empirical rule, analogous to  
the rule for classical boxplots. We illustrate the construction of a  
functional boxplot on a series of sea surface temperatures related to  
the El Nino phenomenon and its outlier detection performance is  
explored by simulations. As applications, the functional boxplot is  
demonstrated on spatio-temporal U.S. precipitation data for nine  
climatic regions and on climate general circulation model (GCM)  
output. Further adjustments of the functional boxplot for outlier  
detection in spatio-temporal data are discussed as well. The talk is  
based on joint work with Ying Sun.

The abstract is also to be found here:  http://stat.ethz.ch/events/research_seminar
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