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Abstract Details
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Activity Number:
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596
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #304963 |
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Title:
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On Likelihood-Based Inference for Spatial Linear Model
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Author(s):
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Jun Zhu*+
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Companies:
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University of Wisconsin-Madison
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Address:
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Department of Statistics, Madison, WI, 53706, United States
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Keywords:
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Abstract:
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Of interest is statistical inference for spatial linear models such that the random error is a Gaussian process with either a continuous or a discrete spatial index. With a continuous spatial index, maximum likelihood and its covariance-tapered counterpart is considered for estimation of the model parameters. Asymptotic properties are investigated under an increasing domain and compared with those under infill asymptotics. With a discrete spatial index, maximum likelihood estimation is studied and asymptotic properties are examined under both increasing domain and infill asymptotics. Computer simulation studies are conducted to evaluate finite-sample properties.
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Authors who are presenting talks have a * after their name.
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