A stabilized and versatile spatial prediction method for geostatistical models

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题名:	A stabilized and versatile spatial prediction method for geostatistical models
作者:	陳春樹;Chen, Chun-Shu;Yang, Hong-Ding;Li, Yang
贡献者:	理學院統計研究所
关键词:	Correlation;data perturbation;Estimates;estimation uncertainty;Geostatistics;Mathematical analysis;Mathematical models;mean squared prediction error;model averaging;Perturbation methods;Precipitation;Stein's unbiased risk estimate;Uncertainty
日期:	2014-01-01
上传时间:	2026-04-23 12:48:02 (UTC+8)
出版者:	John Wiley and Sons Ltd;Blackwell Publishing Ltd
摘要:	摘要： Geostatistical models are often used to predict spatial variables of interest, but the parameters involved in the spatial correlation function usually cannot be well estimated even if the sample size is large enough under a fixed domain. This would result in the spatial predictor being inaccurate and unstable. In this paper, we apply a data perturbation technique to obtain a new spatial predictor, which is not only continuous but also differentiable with respect to the response variables even after plugging‐in the estimated model parameters. Therefore, it is more stable. Moreover, it is known that different spatial predictors obtained from different methods generally have different levels of performance under different circumstances. To mitigate the uncertainties inherent in a selection process, in this paper, we propose an estimation method of weights based on Stein's unbiased risk estimate to combine candidate stabilized spatial predictors, leading to a versatile spatial predictor that is adaptive to the underlying spatial process. Validity for the proposed spatial prediction method is justified both numerically and theoretically. Furthermore, an application of a real data set for the precipitation levels in the Colorado State is also presented. Copyright © 2014 John Wiley & Sons, Ltd. 其他題名： Environmetrics 出版者： Blackwell Publishing Ltd 出版日期： 2014-03 出處： Environmetrics (London, Ont.), 2014-03, Vol.25 (2), p.127-141 資源來源： Wiley Online Library - AutoHoldings Journals 版權： Copyright © 2014 John Wiley & Sons, Ltd. 識別號： ISSN: 1180-4009 識別號： EISSN: 1099-095X 識別號： DOI: 10.1002/env.2263
显示于类别:	[Graduate Institute of Statistics] journal & Dissertation

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