An Alternative Measure of the Reliability of Ordinary Kriging Estimates

By Jorge Kazuo Yamamoto. 2000

196 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0c043947a0d291e78967495b8d83adbf112afe2d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Martin Charlton
2. Paul Harris
3. bullet A Stewart Fotheringham
4. Vladimir Balabanov
5. Timothy W. Simpson
6. Felipe Antonio Chegury Viana
7. V. Toropov
8. V. Roshan Joseph

In Environmental Engineering

1. Paul Harris
2. Martin Charlton
3. bullet A Stewart Fotheringham
4. Vladimir Balabanov
5. Timothy W. Simpson
6. Felipe Antonio Chegury Viana
7. V. Toropov
8. V. Roshan Joseph

In Statistics and Probability

1. Felipe Antonio Chegury Viana
2. Vladimir Balabanov
3. V. Toropov
4. Martin Charlton
5. Paul Harris
6. bullet A Stewart Fotheringham
7. Timothy W. Simpson
8. V. Roshan Joseph

Citations in the corpus

• Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “prediction variance [87,88], the use of kriging in variable fidelity”
• Moving window kriging with geographically weighted variograms (2010, methodology): “…(BCK) (Kitanidis and Shen 1996), kriging with locally varying sills (Kls) (Isaaks and Srivastava 1989), kriging with an interpolation variance (Kiv) (Yamamoto 2000), kriging within strata (StK) (Stein et al. 1988), kriging within classes (CK) (Cressie 1993), multiple population indicator kriging…”
• Correcting the Smoothing Effect of Ordinary Kriging Estimates (2005, background): “ Yamamoto (2000, p. 493) also showed that the deficit of variance could be expressed in terms of the interpolation variance as:”
• ZipWeave: Towards efficient and reliable measurement based mobile coverage maps (2017, background): “Hence, it is homoescedastic and may prove untrustworthy when there is a lack of stationarity accross the region of interest [17].”
• Composite Gaussian process models for emulating expensive functions (2012, background): “Apart from prediction, another frequently noted drawback of ordinary kriging is the poor coverage of its prediction intervals [Yamamoto (2000), Xiong et al. (2007), Gramacy and Lee (2012), Joseph and Kang (2011)].”
• Global gridded precipitation over land: a description of the new GPCC First Guess Daily product (2013, methodology): “5,20 botton left), calculated according to Yamamoto (2000).”