HR: 0800h
AN: NG11A-0178    [Abstracts]
TI: Multivariate Dependence Estimation in Geophysics
AU: * Ganguly, A R
EM: gangulyar@ornl.gov
AF: Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, United States
AU: Khan, S
AF: AIR Worldwide Corporation, 131 Dartmouth Street, Boston, MA 02116, United States
AU: Bandyopadhyay, S
AF: Institute for Systems Research, 3119 A.V. Williams Building University of Maryland, College Park, MD 20742, United States
AB: Multivariate correlation is a useful tool for statistical analysis and prediction, and for developing or validating physically-based models. To illustrate, linear correlation is a basic tool for exploratory data analysis, and for predictive analysis, while linear auto- and cross-correlation functions, or their Fourier transforms corresponding to spectral or cross-spectral methods, are building blocks for time series analysis and prediction. Spatial auto- and cross-correlation functions and corresponding spectral methods are often the first steps in spatial statistics. However, one major gap in the literature is the limited applicability of rigorous approaches to quantify multivariate dependence structures beyond mere linear associations. While information theoretic measures based on mutual information capture the complete dependence in principle, useful recipes for estimating the mutual information from data are still emerging, and in most cases, remain untested on short and noisy, real-world data. A second major gap is the quantification of the associations within and among anomalies and extreme values, even though new approaches, for example based on copula estimation, have been proposed. Third, measures for multivariate dependence in space and time are still in their infancy. Here the state-of-the-art for multivariate dependence estimation in geophysical problems is described, with particular emphasis on new challenges, emerging methods, and real-world applications.
DE: 4400 NONLINEAR GEOPHYSICS (3200, 6944, 7839)
SC: Nonlinear Geophysics [NG]
MN: 2007 Fall Meeting