On spatiotemporal prediction for on-line monitoring data

被引:6
作者
Berke, O [1 ]
机构
[1] Univ Dortmund, Dept Stat, D-44221 Dortmund, Germany
关键词
geostatistics; spatiotemporal dynamic linear models; trend surface analysis; universal kriging; Kalman filtering; empirical best linear unbiased estimation; linear Bayesian methods;
D O I
10.1080/03610929808832231
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Spatiotemporal prediction is of interest in many areas of applied statistics, especially in environmental monitoring with on-line data information. At first, this article reviews the approaches for spatiotemporal modeling in the context; of stochastic processes and then introduces the new class of spatiotemporal dynamic linear models. Further, the methods for linear spatial data analysis, universal kriging and trend surface prediction, are related to the method of spatial linear Bayesian analysis. The Kalman filter is the preferred method for temporal linear Bayesian inferences. By combining the Kalman filter recursions with the trend surface predictor and universal kriging predictor, the prior and posterior spatiotemporal predictors for the observational process are derived, which form the main result of this article. The problem of spatiotemporal linear prediction in the case of unknown first and second order moments is treated as well.
引用
收藏
页码:2343 / 2369
页数:27
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