Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328106229> ?p ?o ?g. }
- W4328106229 endingPage "100742" @default.
- W4328106229 startingPage "100742" @default.
- W4328106229 abstract "Understanding and predicting environmental phenomena often requires the construction of spatio-temporal statistical models, which are typically Gaussian processes. A common assumption made on Gaussian processes is that of covariance stationarity, which is unrealistic in many geophysical applications. In this article, we introduce a deep-learning-inspired approach to construct descriptive nonstationary spatio-temporal models by modeling stationary processes on warped spatio-temporal domains. The warping functions we use are constructed using several simple injective warping units which, when combined through composition, can induce complex warpings. A stationary spatio-temporal covariance function on the warped domain induces covariance nonstationarity on the original domain. Sparse linear algebraic methods are used to reduce the computational complexity when fitting the model in a big data setting. We show that our proposed nonstationary spatio-temporal model can capture covariance nonstationarity in both space and time, and provide better probabilistic predictions than conventional stationary models in both simulation studies and on a real-world data set." @default.
- W4328106229 created "2023-03-22" @default.
- W4328106229 creator A5008675494 @default.
- W4328106229 creator A5063727990 @default.
- W4328106229 creator A5078185918 @default.
- W4328106229 date "2023-04-01" @default.
- W4328106229 modified "2023-10-01" @default.
- W4328106229 title "Constructing large nonstationary spatio-temporal covariance models via compositional warpings" @default.
- W4328106229 cites W1498436455 @default.
- W4328106229 cites W1524392826 @default.
- W4328106229 cites W1837874438 @default.
- W4328106229 cites W1898904249 @default.
- W4328106229 cites W2004777611 @default.
- W4328106229 cites W2004807582 @default.
- W4328106229 cites W2013627498 @default.
- W4328106229 cites W2017195198 @default.
- W4328106229 cites W2023799692 @default.
- W4328106229 cites W2025720061 @default.
- W4328106229 cites W2040812301 @default.
- W4328106229 cites W2050452731 @default.
- W4328106229 cites W2050497240 @default.
- W4328106229 cites W2070620886 @default.
- W4328106229 cites W2071296325 @default.
- W4328106229 cites W2079263973 @default.
- W4328106229 cites W2213174481 @default.
- W4328106229 cites W2276712355 @default.
- W4328106229 cites W2510190756 @default.
- W4328106229 cites W2519630975 @default.
- W4328106229 cites W2581987418 @default.
- W4328106229 cites W2586419555 @default.
- W4328106229 cites W2760941702 @default.
- W4328106229 cites W2779868453 @default.
- W4328106229 cites W2794669705 @default.
- W4328106229 cites W2795443545 @default.
- W4328106229 cites W2799211855 @default.
- W4328106229 cites W2807234722 @default.
- W4328106229 cites W2810491782 @default.
- W4328106229 cites W2913808343 @default.
- W4328106229 cites W2949860763 @default.
- W4328106229 cites W2952060454 @default.
- W4328106229 cites W2963513094 @default.
- W4328106229 cites W3002665913 @default.
- W4328106229 cites W3002997980 @default.
- W4328106229 cites W3022958247 @default.
- W4328106229 cites W3102144874 @default.
- W4328106229 cites W3114272883 @default.
- W4328106229 cites W3130839954 @default.
- W4328106229 cites W3131234345 @default.
- W4328106229 cites W3134488295 @default.
- W4328106229 cites W3169688096 @default.
- W4328106229 doi "https://doi.org/10.1016/j.spasta.2023.100742" @default.
- W4328106229 hasPublicationYear "2023" @default.
- W4328106229 type Work @default.
- W4328106229 citedByCount "2" @default.
- W4328106229 countsByYear W43281062292023 @default.
- W4328106229 crossrefType "journal-article" @default.
- W4328106229 hasAuthorship W4328106229A5008675494 @default.
- W4328106229 hasAuthorship W4328106229A5063727990 @default.
- W4328106229 hasAuthorship W4328106229A5078185918 @default.
- W4328106229 hasBestOaLocation W43281062292 @default.
- W4328106229 hasConcept C105795698 @default.
- W4328106229 hasConcept C11413529 @default.
- W4328106229 hasConcept C114289077 @default.
- W4328106229 hasConcept C118006245 @default.
- W4328106229 hasConcept C121332964 @default.
- W4328106229 hasConcept C137250428 @default.
- W4328106229 hasConcept C148893098 @default.
- W4328106229 hasConcept C154945302 @default.
- W4328106229 hasConcept C163716315 @default.
- W4328106229 hasConcept C178650346 @default.
- W4328106229 hasConcept C33923547 @default.
- W4328106229 hasConcept C41008148 @default.
- W4328106229 hasConcept C61326573 @default.
- W4328106229 hasConcept C62520636 @default.
- W4328106229 hasConcept C83042196 @default.
- W4328106229 hasConcept C88516994 @default.
- W4328106229 hasConceptScore W4328106229C105795698 @default.
- W4328106229 hasConceptScore W4328106229C11413529 @default.
- W4328106229 hasConceptScore W4328106229C114289077 @default.
- W4328106229 hasConceptScore W4328106229C118006245 @default.
- W4328106229 hasConceptScore W4328106229C121332964 @default.
- W4328106229 hasConceptScore W4328106229C137250428 @default.
- W4328106229 hasConceptScore W4328106229C148893098 @default.
- W4328106229 hasConceptScore W4328106229C154945302 @default.
- W4328106229 hasConceptScore W4328106229C163716315 @default.
- W4328106229 hasConceptScore W4328106229C178650346 @default.
- W4328106229 hasConceptScore W4328106229C33923547 @default.
- W4328106229 hasConceptScore W4328106229C41008148 @default.
- W4328106229 hasConceptScore W4328106229C61326573 @default.
- W4328106229 hasConceptScore W4328106229C62520636 @default.
- W4328106229 hasConceptScore W4328106229C83042196 @default.
- W4328106229 hasConceptScore W4328106229C88516994 @default.
- W4328106229 hasLocation W43281062291 @default.
- W4328106229 hasLocation W43281062292 @default.
- W4328106229 hasLocation W43281062293 @default.
- W4328106229 hasOpenAccess W4328106229 @default.
- W4328106229 hasPrimaryLocation W43281062291 @default.
- W4328106229 hasRelatedWork W143964308 @default.