Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/DADCB779-C8D0-4433-9745-62450E9AB794 | - |
| Identifier | https://link.springer.com/article/10.1007%2Fs00477-016-1361-0 | - |
| Identifier | https://doi.org/10.1007/s00477-016-1361-0 | - |
| Language | en | - |
| Extent | 18 pages | en |
| Title | Non-parametric approximations for anisotropy estimation in two-dimensional differentiable Gaussian random fields | en |
| Creator | Petrakis Emmanouil | en |
| Creator | Πετρακης Εμμανουηλ | el |
| Creator | Christopoulos Dionysios | en |
| Creator | Χριστοπουλος Διονυσιος | el |
| Publisher | Springer Verlag | en |
| Content Summary | Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2018-04-16 | - |
| Date of Publication | 2017 | - |
| Subject | Anisotropy | en |
| Subject | Isotropy | en |
| Subject | Monte Carlo simulations | en |
| Subject | Nonparametric | en |
| Subject | Probability regions | en |
| Subject | Radiation exposure | en |
| Bibliographic Citation | M. P. Petrakis and D. T. Hristopulos, "Non-parametric approximations for anisotropy estimation in two-dimensional differentiable Gaussian random fields," Stoch. Env. Res. and Risk A, vol. 31, no. 7, pp. 1853-1870, Sept. 2017. doi:10.1007/s00477-016-1361-0 | en |
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