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Activity Number:
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484
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Type:
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Invited
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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International Indian Statistical Association
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| Abstract - #304994 |
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Title:
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Minimum Distance Inference in Unilateral Autoregressive Lattice Processes
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Author(s):
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Marc G. Genton*+ and Hira L. Koul
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Companies:
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Texas A&M University and Michigan State University
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Address:
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Department of Statistics, College Station, TX, 77843-3143,
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Keywords:
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autoregressive ; lattice ; least squares ; minimum distance ; spatial ; time series
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Abstract:
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We discuss two classes of minimum distance estimators of the underlying parameters and their robust variants in unilateral autoregressive lattice models. We present an asymptotically distribution free test for testing the symmetry of the error distribution, a goodness-of-fit test for fitting an error distribution, and a lack-of-fit test for the hypothesis that the given process is doubly geometric based on the least absolute deviation residuals. A simulation study investigates some small sample properties of the estimators and their robustness. It shows that some of the proposed estimators are more efficient than the least squares estimator at non-normal error distributions. We also study the empirical level and power of the test of a doubly geometric process at various error distributions. Our methodology is then applied to a real data set of yields from an agricultural experiment.
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