Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/2078BCCC-88AE-406C-82F0-17CFA426BABC | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.spasta.2019.100403 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S221167531930154X | - |
| Γλώσσα | en | - |
| Μέγεθος | 22 pages | en |
| Μέγεθος | 3,42 megabytes | en |
| Τίτλος | Stochastic local interaction model with sparse precision matrix for space-time interpolation | en |
| Δημιουργός | Christopoulos Dionysios | en |
| Δημιουργός | Χριστοπουλος Διονυσιος | el |
| Δημιουργός | Agou Vasiliki | en |
| Δημιουργός | Αγου Βασιλικη | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | This article belongs to a Special issue dedicated to the 9th METMA conference which took place in Montpellier (France) from June 13 to 15, 2018. METMA 2018: Space–time modeling of rare events and environmental risks. | el |
| Περίληψη | The application of geostatistical and machine learning methods based on Gaussian processes to big space–time data is beset by the requirement for storing and numerically inverting large and dense covariance matrices. Computationally efficient representations of space–time correlations can be constructed using local models of conditional dependence which can reduce the computational load. We formulate a stochastic local interaction model for regular and scattered space–time data that incorporates interactions within controlled space–time neighborhoods. The strength of the interaction and the size of the neighborhood are defined by means of kernel functions and adaptive local bandwidths. Compactly supported kernels lead to finite-size local neighborhoods and consequently to sparse precision matrices that admit explicit expression. Hence, the stochastic local interaction model’s requirements for storage are modest and the costly covariance matrix inversion is not needed. We also derive a semi-explicit prediction equation and express the conditional variance of the prediction in terms of the diagonal of the precision matrix. For data on regular space–time lattices, the stochastic local interaction model is equivalent to a Gaussian Markov Random Field. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2021-03-31 | - |
| Ημερομηνία Δημοσίευσης | 2020 | - |
| Θεματική Κατηγορία | Scattered data | en |
| Θεματική Κατηγορία | Sparse precision matrix | en |
| Θεματική Κατηγορία | Space–time interpolation | en |
| Θεματική Κατηγορία | Gaussian Markov random field | en |
| Θεματική Κατηγορία | Stochastic process on graph | en |
| Θεματική Κατηγορία | Space–time kernel | en |
| Βιβλιογραφική Αναφορά | D. T. Hristopulos and V. D. Agou, “Stochastic local interaction model with sparse precision matrix for space–time interpolation”, Spat. Stat., vol. 40, Dec. 2020. doi: 10.1016/j.spasta.2019.100403 | en |
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