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Abstract Details
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Activity Number:
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363
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #302634 |
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Title:
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On the Asymptotics of Maximum Likelihood Estimation for Spatial-Temporal Linear Models on a Lattice
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Author(s):
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Xiang Zhang*+ and Yanbing Zheng
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Companies:
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University of Kentucky and University of Kentucky
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Address:
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, , 40508,
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Keywords:
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Autoregressive models ;
increasing domain asymptotics ;
linear regression ;
spatial-temporal process
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Abstract:
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spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data and have been applied in a wide range of disciplines. However, understanding of the asymptotic properties of maximum likelihood estimates under general asymptotic framework is limited. Here we focus on spatial-temporal simultaneous autoregressive models. Under increasing domain asymptotic framework, we propose mild regularity conditions on the spatial-temporal weight matrices and derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates.
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