{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:RVSGYTLAEDYNCA2R6DXSNB4GLV","short_pith_number":"pith:RVSGYTLA","canonical_record":{"source":{"id":"1503.02912","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-03-10T14:06:55Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"7230b950189b63c32590d5593a0cfd9b07bec41a5769cf2f32f432df9e2a4a54","abstract_canon_sha256":"4fb5baabe365d0eb9f79642bcac5cf279d57481b660a203fcd40668f6a3af4a9"},"schema_version":"1.0"},"canonical_sha256":"8d646c4d6020f0d10351f0ef2687865d4e2ceb043cf1bd146e9bee272ee8016f","source":{"kind":"arxiv","id":"1503.02912","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.02912","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"arxiv_version","alias_value":"1503.02912v4","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.02912","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"pith_short_12","alias_value":"RVSGYTLAEDYN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RVSGYTLAEDYNCA2R","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RVSGYTLA","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:RVSGYTLAEDYNCA2R6DXSNB4GLV","target":"record","payload":{"canonical_record":{"source":{"id":"1503.02912","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-03-10T14:06:55Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"7230b950189b63c32590d5593a0cfd9b07bec41a5769cf2f32f432df9e2a4a54","abstract_canon_sha256":"4fb5baabe365d0eb9f79642bcac5cf279d57481b660a203fcd40668f6a3af4a9"},"schema_version":"1.0"},"canonical_sha256":"8d646c4d6020f0d10351f0ef2687865d4e2ceb043cf1bd146e9bee272ee8016f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:14.275737Z","signature_b64":"77J0UQowTe2b2zzUm3Xf9e139W57D+sGC4rHn9L5jJRHVTNAfk/rg9ksKDLvT3FuMM8q6IxUc1H4Nh5klmniDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d646c4d6020f0d10351f0ef2687865d4e2ceb043cf1bd146e9bee272ee8016f","last_reissued_at":"2026-05-18T00:40:14.275043Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:14.275043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.02912","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:40:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YXtyFv4ZuypevEVfXP48hNc5Eq9E49stBLo9xNn5J/TC7zOUywjL980GmSnkndpTnKt5mSWh7DboCEt+Y6+iBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:51:19.877094Z"},"content_sha256":"99ed72ba34b7c1bbfc4fe782291c27244f0a2fe8af68e3af26f29656c9e856b4","schema_version":"1.0","event_id":"sha256:99ed72ba34b7c1bbfc4fe782291c27244f0a2fe8af68e3af26f29656c9e856b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:RVSGYTLAEDYNCA2R6DXSNB4GLV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximate Bayesian inference in semiparametric copula models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Brunero Liseo, Clara Grazian","submitted_at":"2015-03-10T14:06:55Z","abstract_excerpt":"We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighed in terms of their empirical likelihood. This method is particularly useful when the \"true\" likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.02912","kind":"arxiv","version":4},"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":"2026-05-18T00:40:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lNQDowtjzE+qi7oXjuigVS3ygjSHCMdkCh/9lz47LS03beIx6gLv/9LMwS1uAn5kItVcKXpZloOsoDWnKveyAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T17:51:19.877742Z"},"content_sha256":"6c1c9ee167cb5d7abc782fd0726a2c70db52c44e97453a7e459cd0ceba31f3d2","schema_version":"1.0","event_id":"sha256:6c1c9ee167cb5d7abc782fd0726a2c70db52c44e97453a7e459cd0ceba31f3d2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/bundle.json","state_url":"https://pith.science/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/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":"2026-05-19T17:51:19Z","links":{"resolver":"https://pith.science/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV","bundle":"https://pith.science/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/bundle.json","state":"https://pith.science/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RVSGYTLAEDYNCA2R6DXSNB4GLV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:RVSGYTLAEDYNCA2R6DXSNB4GLV","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":"4fb5baabe365d0eb9f79642bcac5cf279d57481b660a203fcd40668f6a3af4a9","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-03-10T14:06:55Z","title_canon_sha256":"7230b950189b63c32590d5593a0cfd9b07bec41a5769cf2f32f432df9e2a4a54"},"schema_version":"1.0","source":{"id":"1503.02912","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.02912","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"arxiv_version","alias_value":"1503.02912v4","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.02912","created_at":"2026-05-18T00:40:14Z"},{"alias_kind":"pith_short_12","alias_value":"RVSGYTLAEDYN","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"RVSGYTLAEDYNCA2R","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"RVSGYTLA","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:6c1c9ee167cb5d7abc782fd0726a2c70db52c44e97453a7e459cd0ceba31f3d2","target":"graph","created_at":"2026-05-18T00:40:14Z","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":"We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian Monte Carlo algorithm, where the proposed values of the functional of interest are weighed in terms of their empirical likelihood. This method is particularly useful when the \"true\" likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.","authors_text":"Brunero Liseo, Clara Grazian","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-03-10T14:06:55Z","title":"Approximate Bayesian inference in semiparametric copula models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.02912","kind":"arxiv","version":4},"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":"sha256:99ed72ba34b7c1bbfc4fe782291c27244f0a2fe8af68e3af26f29656c9e856b4","target":"record","created_at":"2026-05-18T00:40:14Z","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":"4fb5baabe365d0eb9f79642bcac5cf279d57481b660a203fcd40668f6a3af4a9","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-03-10T14:06:55Z","title_canon_sha256":"7230b950189b63c32590d5593a0cfd9b07bec41a5769cf2f32f432df9e2a4a54"},"schema_version":"1.0","source":{"id":"1503.02912","kind":"arxiv","version":4}},"canonical_sha256":"8d646c4d6020f0d10351f0ef2687865d4e2ceb043cf1bd146e9bee272ee8016f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d646c4d6020f0d10351f0ef2687865d4e2ceb043cf1bd146e9bee272ee8016f","first_computed_at":"2026-05-18T00:40:14.275043Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:40:14.275043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"77J0UQowTe2b2zzUm3Xf9e139W57D+sGC4rHn9L5jJRHVTNAfk/rg9ksKDLvT3FuMM8q6IxUc1H4Nh5klmniDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:40:14.275737Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.02912","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99ed72ba34b7c1bbfc4fe782291c27244f0a2fe8af68e3af26f29656c9e856b4","sha256:6c1c9ee167cb5d7abc782fd0726a2c70db52c44e97453a7e459cd0ceba31f3d2"],"state_sha256":"f323955e9523a71cacaffc30978f695412b160c6bff5ec4875ce9493057c532c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AV4vXU1yMMvmzpE70jD7Hp47Ux5KKgv0aJD3y+tEAt0rwvnboHPRr4yKozsXSOBBt57eqdRdlS/nkZAiqtsaCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T17:51:19.881281Z","bundle_sha256":"a497ba904e627ac70381d256e35328bafc79729ce676c0548e0fa7892648da43"}}