ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476752

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476753

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the stations in the U.S. historical climatological network. The stochastic structure is modeled by a stationary spatial-temporal Gaussian random field. For this region, we find little evidence of temporal dependence while the spatial structure is temporally stable. The approach strives to incorporate the uncertainty in estimating the covariance structure into the predictive distributions and the final inference. As an application of the model, we derive posterior distributions of the areal mean over time. A posterior distribution for the static areal mean is presented as a basis for calibrating temperature shifts by the historical record. For this region and season, the distribution indicates that under the scenario of a gradual increase of 5°F over 50 years, it will take 30–40 winters of data before the change will be discernible from the natural variation in temperatures.

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476754

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476755

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476756

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476757

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476758

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

In disciplines such as soil science, ecology, meteorology, water resources, mining engineering, and forestry, spatial prediction is of central interest. A sparsely sampled spatial process yields imperfect knowledge of a resource, from which prediction of unobserved parts of the process are to be made. A popular stochastic method that solves this problem is kriging. But the appropriateness of kriging—and, for that matter, of any method based on probabilistic models for spatial data—has been frequently questioned. A number of nonstochastic methods have also been proposed, the leading contender of which appears to be splines. There has been some debate as to which of kriging and splines is better—a debate that has centered largely on operational issues, because the two methods are based on different models for the process. In this article the debate is turned to where it ultimately matters—namely, the precision of prediction based on real data. By dividing data sets into modeling sets and prediction sets, the two methods may be compared. In the cases examined, kriging sometimes outperforms splines by a considerable margin, and it never performs worse than splines. Various configurations of data show that the sampling regime determines when kriging will outperform splines.

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476759

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476760

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1994.10476761

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor