{"paper":{"title":"Combining covariance tapering and lasso driven low rank decomposition for the kriging of large spatial datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Nicolas Desassis, Thomas Romary (GEOSCIENCES)","submitted_at":"2018-06-05T08:44:44Z","abstract_excerpt":"Large spatial datasets are becoming ubiquitous in environmental  sciences with the explosion in the amount of data produced by  sensors that monitor and measure the Earth system. Consequently, the  geostatistical analysis of these data requires adequate methods.  Richer datasets lead to more complex modeling but may also prevent  from using classical techniques. Indeed, the kriging predictor is  not straightforwarldly available as it requires the inversion of the  covariance matrix of the data. The challenge of handling such  datasets is therefore to extract the maximum of information they  co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01558","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}