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Abstract Details

Activity Number: 596
Type: Topic Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #304963
Title: On Likelihood-Based Inference for Spatial Linear Model
Author(s): Jun Zhu*+
Companies: University of Wisconsin-Madison
Address: Department of Statistics, Madison, WI, 53706, United States

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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