[Statlist] Seminar ueber Statistik

Christina Kuenzli kuenz|| @end|ng |rom @t@t@m@th@ethz@ch
Tue Nov 30 15:58:15 CET 2004


                       ETH and University of Zurich 
      Proff. A.D. Barbour -- P. Buehlmann -- F. Hampel -- H.R. Kuensch 

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           We are pleased to announce the following talk
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Friday, December 3rd, 2004, 15.15 h, LEO C 15

      Gabriel Frahm, Center of Advanced European Studies and Research, Bonn

      Random Matrix Theory and Robust Covariance Matrix Estimation
      for Generalized Elliptical Distributions
             
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Friday, December 17, 2004, 15.15 h, LEO C 15

      Reinhard Furrer, National Center for Atmospheric Research
                       Boulder, Colorado

      Covariance Tapering for Interpolation of Large Spatial Datasets

Interpolation of a spatially correlated random process is used in many
areas. The best unbiased linear predictor, often called kriging in
geostatistical science, requires the solution of a large linear system
based on the covariance matrix of the observations. As a motivating
example for climate science, estimating monthly precipitation fields
over the US involves more than 5900 station location at its peak
network size and must be repeated over the more than 1200 months of
the historical record. Each estimated field should be evaluated on a
fine grid of resolution of approximately $1000\times1000$. Tapering
the correct covariance matrix with an appropriate compactly supported
covariance function reduces the computational burden significantly and
still has an asymptotic optimal mean squared error. The effect of
tapering is to create a sparse approximate linear system that can then
be solved using sparse matrix algorithms. In the precipitation dataset
one achieves a speedup by more than 500 to solve the linear system.
Further, the manageable size of the observed and predicted fields can
be far bigger than with classical approaches. The net result is the
ability to analyze spatial data sets that are several orders of
magnitude larger than past work in a high level interactive environment
such as R.
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(LEO (Leonhardstrasse 27, 8006 Zurich) is close to the main building,
across the hill-side station of the 'Polybahn') 
Overview maps of ETH : http://www.ethz.ch/search/orientation_en.asp

Further information: Christina Kuenzli, Statistics Seminar of ETH Zurich 
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Everybody is kindly invited
 
Eidgenoessische Technische Hochschule Zuerich
Swiss Federal Insitute of Technology Zurich

________________________________________________________
Christina Kuenzli            <kuenzli using stat.math.ethz.ch>
Seminar fuer Statistik      
Leonhardstr. 27,  LEO D11          phone: +41 1 632 3438         
ETH-Zentrum,                       fax  : +41 1 632 1228 
CH-8092 Zurich, Switzerland        http://stat.ethz.ch/~




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