This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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82
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #308521 |
Title:
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Semiparametric Estimation and Selection for Nonstationary Spatial Covariance Functions
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Author(s):
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Nan-Jung Hsu*+ and Hsin-Cheng Huang and Ya-Mei Chang
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Companies:
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National Tsing-Hua University and Academia Sinica and CSIRO
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Address:
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101, Section 2, Kuang-Fu Rd., Hsin-Chu, 300, Taiwan
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Keywords:
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constrained least squares ;
positive Lasso ;
spatial prediction
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Abstract:
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We propose a method for estimating nonstationary spatial covariance functions by representing a spatial process as a linear combination of some local basis functions with uncorrelated random coefficients and some stationary processes, based on spatial data sampled in space with repeated measurements. The covariance estimation and model selection are formulated as a regression problem with the sample covariances as the response and the covariances corresponding to the local basis functions and the stationary processes as the predictors. A constrained least squares approach is applied to select appropriate basis functions and stationary processes as well as estimates parameters simultaneously. A simulation experiment shows that our method performs well in both covariance function estimation and spatial prediction. The methodology is applied to the U.S. precipitation data for illustration.
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