Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Stochastic local interaction model with sparse precision matrix for space-time interpolation

Christopoulos Dionysios, Agou Vasiliki

Simple record


URIhttp://purl.tuc.gr/dl/dias/2078BCCC-88AE-406C-82F0-17CFA426BABC-
Identifierhttps://doi.org/10.1016/j.spasta.2019.100403-
Identifierhttps://www.sciencedirect.com/science/article/pii/S221167531930154X-
Languageen-
Extent22 pagesen
Extent3,42 megabytesen
TitleStochastic local interaction model with sparse precision matrix for space-time interpolationen
CreatorChristopoulos Dionysiosen
CreatorΧριστοπουλος Διονυσιοςel
CreatorAgou Vasilikien
CreatorΑγου Βασιλικηel
PublisherElsevieren
DescriptionThis 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
Content SummaryThe 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
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-03-31-
Date of Publication2020-
SubjectScattered dataen
SubjectSparse precision matrixen
SubjectSpace–time interpolationen
SubjectGaussian Markov random fielden
SubjectStochastic process on graphen
SubjectSpace–time kernelen
Bibliographic CitationD. 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.2019.100403en

Services

Statistics