{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:RHGZCP2DGSQDGL73WWRYPSU7S2","short_pith_number":"pith:RHGZCP2D","canonical_record":{"source":{"id":"1403.4291","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-03-17T22:28:14Z","cross_cats_sorted":["q-fin.RM","stat.AP"],"title_canon_sha256":"2acde9871e89e57f05c25d7f8f2e807e27727b64f2c88ccd0f9929f6be4a1560","abstract_canon_sha256":"c2ec051a71407eb17062677b51165c8cc05878a3d90586f1ecc93bef7fc29829"},"schema_version":"1.0"},"canonical_sha256":"89cd913f4334a0332ffbb5a387ca9f9696585189a01f938d923c5f19b303714d","source":{"kind":"arxiv","id":"1403.4291","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.4291","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"arxiv_version","alias_value":"1403.4291v3","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.4291","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"pith_short_12","alias_value":"RHGZCP2DGSQD","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_16","alias_value":"RHGZCP2DGSQDGL73","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_8","alias_value":"RHGZCP2D","created_at":"2026-05-18T12:28:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:RHGZCP2DGSQDGL73WWRYPSU7S2","target":"record","payload":{"canonical_record":{"source":{"id":"1403.4291","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-03-17T22:28:14Z","cross_cats_sorted":["q-fin.RM","stat.AP"],"title_canon_sha256":"2acde9871e89e57f05c25d7f8f2e807e27727b64f2c88ccd0f9929f6be4a1560","abstract_canon_sha256":"c2ec051a71407eb17062677b51165c8cc05878a3d90586f1ecc93bef7fc29829"},"schema_version":"1.0"},"canonical_sha256":"89cd913f4334a0332ffbb5a387ca9f9696585189a01f938d923c5f19b303714d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:19:28.609724Z","signature_b64":"6QWnkKdBg5hI3cGQNrwNxSKaNfTUqoF7OMREcGqr40CbRuFLl9wqn49JHtjZMokQb2AFcpGfkuFsOo8afMxCCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89cd913f4334a0332ffbb5a387ca9f9696585189a01f938d923c5f19b303714d","last_reissued_at":"2026-05-18T02:19:28.609110Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:19:28.609110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1403.4291","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:19:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V9+Tdm4KLVkfDVB8w9PDkVpePJGrbBXWgPR3qIZf4eT6Q2SC21Cgp5garvLURMcceZ4tuZCdX06J4adg4f+bAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:04:35.224794Z"},"content_sha256":"11b406c50c996cd72d7c0939426ae398a74d1f753dbbba64d571c32a4bcf6dcc","schema_version":"1.0","event_id":"sha256:11b406c50c996cd72d7c0939426ae398a74d1f753dbbba64d571c32a4bcf6dcc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:RHGZCP2DGSQDGL73WWRYPSU7S2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An importance sampling approach for copula models in insurance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.RM","stat.AP"],"primary_cat":"stat.CO","authors_text":"Marius Hofert, Mathieu Cambou, Philipp Arbenz","submitted_at":"2014-03-17T22:28:14Z","abstract_excerpt":"An importance sampling approach for sampling copula models is introduced. We propose two algorithms that improve Monte Carlo estimators when the functional of interest depends mainly on the behaviour of the underlying random vector when at least one of the components is large. Such problems often arise from dependence models in finance and insurance. The importance sampling framework we propose is general and can be easily implemented for all classes of copula models from which sampling is feasible. We show how the proposal distribution of the two algorithms can be optimized to reduce the samp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.4291","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:19:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rLDLLAOvylfno1LR/iOiANmPTFr46xHr2vsLoYOxk3ggEAqZiXcM85H+hE2wSoiw3bYPlz5IWo5T/0TQ2EAsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T10:04:35.225189Z"},"content_sha256":"6c8e56a988fb4ac2ff7582055f3b02382e86e4dc5da2a975a4d75d0e7f2a4a31","schema_version":"1.0","event_id":"sha256:6c8e56a988fb4ac2ff7582055f3b02382e86e4dc5da2a975a4d75d0e7f2a4a31"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/bundle.json","state_url":"https://pith.science/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T10:04:35Z","links":{"resolver":"https://pith.science/pith/RHGZCP2DGSQDGL73WWRYPSU7S2","bundle":"https://pith.science/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/bundle.json","state":"https://pith.science/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RHGZCP2DGSQDGL73WWRYPSU7S2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:RHGZCP2DGSQDGL73WWRYPSU7S2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c2ec051a71407eb17062677b51165c8cc05878a3d90586f1ecc93bef7fc29829","cross_cats_sorted":["q-fin.RM","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-03-17T22:28:14Z","title_canon_sha256":"2acde9871e89e57f05c25d7f8f2e807e27727b64f2c88ccd0f9929f6be4a1560"},"schema_version":"1.0","source":{"id":"1403.4291","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.4291","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"arxiv_version","alias_value":"1403.4291v3","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.4291","created_at":"2026-05-18T02:19:28Z"},{"alias_kind":"pith_short_12","alias_value":"RHGZCP2DGSQD","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_16","alias_value":"RHGZCP2DGSQDGL73","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_8","alias_value":"RHGZCP2D","created_at":"2026-05-18T12:28:46Z"}],"graph_snapshots":[{"event_id":"sha256:6c8e56a988fb4ac2ff7582055f3b02382e86e4dc5da2a975a4d75d0e7f2a4a31","target":"graph","created_at":"2026-05-18T02:19:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"An importance sampling approach for sampling copula models is introduced. We propose two algorithms that improve Monte Carlo estimators when the functional of interest depends mainly on the behaviour of the underlying random vector when at least one of the components is large. Such problems often arise from dependence models in finance and insurance. The importance sampling framework we propose is general and can be easily implemented for all classes of copula models from which sampling is feasible. We show how the proposal distribution of the two algorithms can be optimized to reduce the samp","authors_text":"Marius Hofert, Mathieu Cambou, Philipp Arbenz","cross_cats":["q-fin.RM","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-03-17T22:28:14Z","title":"An importance sampling approach for copula models in insurance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.4291","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:11b406c50c996cd72d7c0939426ae398a74d1f753dbbba64d571c32a4bcf6dcc","target":"record","created_at":"2026-05-18T02:19:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c2ec051a71407eb17062677b51165c8cc05878a3d90586f1ecc93bef7fc29829","cross_cats_sorted":["q-fin.RM","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-03-17T22:28:14Z","title_canon_sha256":"2acde9871e89e57f05c25d7f8f2e807e27727b64f2c88ccd0f9929f6be4a1560"},"schema_version":"1.0","source":{"id":"1403.4291","kind":"arxiv","version":3}},"canonical_sha256":"89cd913f4334a0332ffbb5a387ca9f9696585189a01f938d923c5f19b303714d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89cd913f4334a0332ffbb5a387ca9f9696585189a01f938d923c5f19b303714d","first_computed_at":"2026-05-18T02:19:28.609110Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:19:28.609110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6QWnkKdBg5hI3cGQNrwNxSKaNfTUqoF7OMREcGqr40CbRuFLl9wqn49JHtjZMokQb2AFcpGfkuFsOo8afMxCCg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:19:28.609724Z","signed_message":"canonical_sha256_bytes"},"source_id":"1403.4291","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11b406c50c996cd72d7c0939426ae398a74d1f753dbbba64d571c32a4bcf6dcc","sha256:6c8e56a988fb4ac2ff7582055f3b02382e86e4dc5da2a975a4d75d0e7f2a4a31"],"state_sha256":"46d9208588850d08f63f0d0bbbadc9af40784b0fb847eddbb381f449855c9270"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IcLWwk2CgArHPUn6D05ilDVfAwSQ4sRWaS59gGAcnIeOA3ZEScM+2Pu7trCkrIukpucjLaMmiQDXJtqYtSqhDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T10:04:35.227403Z","bundle_sha256":"f8993cf46b0210c0f533ffab7d34cbd4213c18a621f6e90c57362b42c93d091c"}}