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1. |
W. Edwards Deming 1990–1993 |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 365-366
NancyR. Mann,
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476752
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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2. |
Editors' Report for 1993 |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 367-367
RoderickJ. A. Little,
Myles Hollander,
Alan Agresti,
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PDF (92KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476753
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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3. |
An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 368-378
MarkS. Handcock,
JamesR. Wallis,
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PDF (1443KB)
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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
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4. |
Comment |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 379-382
Noel Cressie,
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PDF (447KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476755
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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5. |
Comment |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 382-384
Peter Guttorp,
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PDF (348KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476756
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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6. |
Comment |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 384-387
ChristianL. Keppenne,
MichaelD. Dettinger,
Michael Ghil,
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PDF (428KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476757
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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7. |
Rejoinder |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 388-390
MarkS. Handcock,
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PDF (361KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476758
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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8. |
Kriging and Splines: An Empirical Comparison of their Predictive Performance in Some Applications |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 391-400
GeoffreyM. Laslett,
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PDF (1265KB)
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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
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9. |
Comment |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 401-403
MarkS. Handcock,
Kristen Meier,
Douglas Nychka,
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PDF (357KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476760
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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10. |
Comments |
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Journal of the American Statistical Association,
Volume 89,
Issue 426,
1994,
Page 403-405
MartinB. Mächler,
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PDF (359KB)
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ISSN:0162-1459
DOI:10.1080/01621459.1994.10476761
出版商:Taylor & Francis Group
年代:1994
数据来源: Taylor
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