期刊
BIOMETRIKA
卷 100, 期 2, 页码 473-484出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ass079
关键词
Covariance estimation; Gaussian process; Infill asymptotics; Matern covariance; Spatial statistics
资金
- Center for Science of Information, an NSF Science and Technology Center
- Statistical and Applied Mathematical Sciences Institute
Two canonical problems in geostatistics are estimating the parameters in a specified family of stochastic process models and predicting the process at new locations. We show that asymptotic results for a Gaussian process over a fixed domain with Matern covariance function, previously proven only in the case of a fixed range parameter, can be extended to the case of jointly estimating the range and the variance of the process. Moreover, we show that intuition and approximations derived from asymptotics using a fixed range parameter can be problematic when applied to finite samples, even for large sample sizes. In contrast, we show via simulation that performance is improved and asymptotic approximations are applicable for smaller sample sizes when the parameters are jointly estimated. These effects are particularly apparent when the process is mean square differentiable or the effective range of spatial correlation is small.
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