Local Prediction of a Spatio-Temporal Process with an Application to Wet Sulfate Deposition
作者:
TimothyC. Haas,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 432
页码: 1189-1199
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476625
出版商: Taylor & Francis Group
关键词: Generalized nonlinear least squares;Kriging;Local regression;Long memory;Semivariogram
数据来源: Taylor
摘要:
A prediction method is given for a first- and second-order nonstationary spatio-temporal process. The predictor uses local data only and consists of a two-stage generalized regression estimate of the local drift at the prediction location added to a kriging prediction of the residual process at that location. This predictor is applied to observations on seasonal, rainfall-deposited sulfate over the conterminous United States between summer 1986 and summer 1992. Analyses suggest that predictions and estimated prediction standard errors have negligible to small biases, there is spatially heterogeneous temporal drift, and temporal covariance is negligible.
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