{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:62EGMYVS4KUCDEE6L5MNLTOVTP","short_pith_number":"pith:62EGMYVS","schema_version":"1.0","canonical_sha256":"f6886662b2e2a821909e5f58d5cdd59bf75d2311e652389f3dd676fdb5b51fd1","source":{"kind":"arxiv","id":"2110.02680","version":1},"attestation_state":"computed","paper":{"title":"Latent Gaussian Models for High-Dimensional Spatial Extremes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Arnab Hazra, \\'Arni V. J\\'ohannesson, Rapha\\\"el Huser","submitted_at":"2021-10-06T12:01:33Z","abstract_excerpt":"In this chapter, we show how to efficiently model high-dimensional extreme peaks-over-threshold events over space in complex non-stationary settings, using extended latent Gaussian Models (LGMs), and how to exploit the fitted model in practice for the computation of long-term return levels. The extended LGM framework assumes that the data follow a specific parametric distribution, whose unknown parameters are transformed using a multivariate link function and are then further modeled at the latent level in terms of fixed and random effects that have a joint Gaussian distribution. In the extrem"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.02680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-10-06T12:01:33Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"fbd1978e1126f15b5357c509bc16a1cead41fe54b666fd27c7e0cbb3f44ebbeb","abstract_canon_sha256":"efb42a5d60540e966212f860016526bcf5e115339def676c274b7582fca31a44"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:20:28.789079Z","signature_b64":"ULqHxyhh2A5fJghpGIKYn1jQKIqh2E69QcgvST77BfUEs0Mf/1qimW0iBd3Wc/UtY0rLAx+RgDCHhCds1J3hAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6886662b2e2a821909e5f58d5cdd59bf75d2311e652389f3dd676fdb5b51fd1","last_reissued_at":"2026-07-05T03:20:28.788661Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:20:28.788661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent Gaussian Models for High-Dimensional Spatial Extremes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Arnab Hazra, \\'Arni V. J\\'ohannesson, Rapha\\\"el Huser","submitted_at":"2021-10-06T12:01:33Z","abstract_excerpt":"In this chapter, we show how to efficiently model high-dimensional extreme peaks-over-threshold events over space in complex non-stationary settings, using extended latent Gaussian Models (LGMs), and how to exploit the fitted model in practice for the computation of long-term return levels. The extended LGM framework assumes that the data follow a specific parametric distribution, whose unknown parameters are transformed using a multivariate link function and are then further modeled at the latent level in terms of fixed and random effects that have a joint Gaussian distribution. In the extrem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.02680","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.02680/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.02680","created_at":"2026-07-05T03:20:28.788722+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.02680v1","created_at":"2026-07-05T03:20:28.788722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.02680","created_at":"2026-07-05T03:20:28.788722+00:00"},{"alias_kind":"pith_short_12","alias_value":"62EGMYVS4KUC","created_at":"2026-07-05T03:20:28.788722+00:00"},{"alias_kind":"pith_short_16","alias_value":"62EGMYVS4KUCDEE6","created_at":"2026-07-05T03:20:28.788722+00:00"},{"alias_kind":"pith_short_8","alias_value":"62EGMYVS","created_at":"2026-07-05T03:20:28.788722+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP","json":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP.json","graph_json":"https://pith.science/api/pith-number/62EGMYVS4KUCDEE6L5MNLTOVTP/graph.json","events_json":"https://pith.science/api/pith-number/62EGMYVS4KUCDEE6L5MNLTOVTP/events.json","paper":"https://pith.science/paper/62EGMYVS"},"agent_actions":{"view_html":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP","download_json":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP.json","view_paper":"https://pith.science/paper/62EGMYVS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.02680&json=true","fetch_graph":"https://pith.science/api/pith-number/62EGMYVS4KUCDEE6L5MNLTOVTP/graph.json","fetch_events":"https://pith.science/api/pith-number/62EGMYVS4KUCDEE6L5MNLTOVTP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP/action/storage_attestation","attest_author":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP/action/author_attestation","sign_citation":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP/action/citation_signature","submit_replication":"https://pith.science/pith/62EGMYVS4KUCDEE6L5MNLTOVTP/action/replication_record"}},"created_at":"2026-07-05T03:20:28.788722+00:00","updated_at":"2026-07-05T03:20:28.788722+00:00"}