Abstract #301475

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JSM 2003 Abstract #301475
Activity Number: 168
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics & the Environment
Abstract - #301475
Title: Three Alternative Estimators for Spatial Parameters
Author(s): Petrutza C. Caragea*+ and Richard L. Smith
Companies: University of North Carolina, Chapel Hill and University of North Carolina
Address: 2 Dansey Cir., Durham, NC, 27713-8644,
Keywords: spatial statistics ; large data sets ; likelihood approximations ; asymptotic relative efficiency
Abstract:

Although computational power has significantly increased during the last few decades, the estimation problem for large spatial data sets is still troublesome. For Gaussian processes, computing the exact maximum likelihood function involves calculation of the inverse and the determinant of the covariance matrix. In practice, this could be problematic even for sample sizes as small as several hundred locations. We propose three approximations to the likelihood, which considerably reduce the number of computations. Furthermore, we analyze the asymptotic efficiency of the alternative estimators, both theoretically (for an analogous time series problem), and through simulation studies. We illustrate the possible impact of these techniques with an analysis of long term trends in precipitation, measuread at approximately 6000 sites across the U.S.

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