Activity Number:
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131
- Methods for Spatial, Temporal, and Spatio-Temporal Data
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #317842
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Title:
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Transformed-Linear Prediction for Extremes
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Author(s):
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Jeongjin Lee* and Daniel Cooley
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Companies:
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Colorado State University and Colorado State University
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Keywords:
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The best transformed-linear prediction;
Tail pairwise dependence matrix;
Completely positive decomposition
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Abstract:
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We derive the best transformed-linear predictor for extremes on the positive orthant within the vector space which is a set of transformed-linear combinations of regularly varying random variables with finite coefficients. The optimized weights for the transformed-linear predictor are represented by the tail pairwise dependence matrix (TPDM) as analogous to those for the BLUP are expressed by a covariance matrix in Gaussian cases. We construct uncertainty quantification using the polar geometry of regular variation utilizing a completely positive decomposition of the TPDM. We produce a 95% joint probability region and 95% conditional prediction intervals given large values of the transformed-linear predictor. We apply our method to the NO2 pollution data in Washington, DC and to daily returns of 30 industry portfolios and obtain good coverage rates.
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Authors who are presenting talks have a * after their name.