{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CNV32A4TSHBXPUJD7J75CCPNS7","short_pith_number":"pith:CNV32A4T","canonical_record":{"source":{"id":"1707.09565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T22:38:43Z","cross_cats_sorted":[],"title_canon_sha256":"c8ec324372a3f46f0513f8711d192d1996e66377185ec51fbb2a9537ecc529aa","abstract_canon_sha256":"999bbfc2ccd8551d5e2369c3b43890a5e38ab3c8bf382bc41166b96339c52412"},"schema_version":"1.0"},"canonical_sha256":"136bbd039391c377d123fa7fd109ed97e2b6598410c8a7f695fde56d673ea0f3","source":{"kind":"arxiv","id":"1707.09565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.09565","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"1707.09565v1","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09565","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"CNV32A4TSHBX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CNV32A4TSHBXPUJD","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CNV32A4T","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CNV32A4TSHBXPUJD7J75CCPNS7","target":"record","payload":{"canonical_record":{"source":{"id":"1707.09565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T22:38:43Z","cross_cats_sorted":[],"title_canon_sha256":"c8ec324372a3f46f0513f8711d192d1996e66377185ec51fbb2a9537ecc529aa","abstract_canon_sha256":"999bbfc2ccd8551d5e2369c3b43890a5e38ab3c8bf382bc41166b96339c52412"},"schema_version":"1.0"},"canonical_sha256":"136bbd039391c377d123fa7fd109ed97e2b6598410c8a7f695fde56d673ea0f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:10.899938Z","signature_b64":"9dCqjOephFhOzf2I821+avTShCdHzz46cGRp5mUkL7EsI9wB3gf52hOUqtT1412aXM1N8+Bfc7wmP5zMpfBxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"136bbd039391c377d123fa7fd109ed97e2b6598410c8a7f695fde56d673ea0f3","last_reissued_at":"2026-05-18T00:39:10.899291Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:10.899291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.09565","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bVopkDPdavf05LabIJf/9J0aphDtnOGtnvxsiaCjs0cq8AbZX6QZBZSf3/qBOgytF1Q2VHzuHbzEvnEt3PRhAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T22:49:25.884001Z"},"content_sha256":"fdc66b0e25783a73d3fee6efbe394ba66d5b14e409840df0e557699de4bee9be","schema_version":"1.0","event_id":"sha256:fdc66b0e25783a73d3fee6efbe394ba66d5b14e409840df0e557699de4bee9be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CNV32A4TSHBXPUJD7J75CCPNS7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Skew-Normal Copula-Driven GLMM","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Arusharka Sen, Kalyan Das, Mohamad Elmasri","submitted_at":"2017-07-29T22:38:43Z","abstract_excerpt":"This paper presents a method for fitting a copula-driven generalized linear mixed models. For added flexibility, the skew-normal copula is adopted for fitting. The correlation matrix of the skew-normal copula is used to capture the dependence structure within units, while the fixed and random effects coefficients are estimated through the mean of the copula. For estimation, a Monte Carlo expectation-maximization algorithm is developed. Simulations are shown alongside a real data example from the Framingham Heart Study."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09565","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"em3pergsGL6HL5Is8P9NqK1LVUYikUfp+0hTQFk/xB+gXyv9KSMU1L7LZq6MR56Zrtb2iHh3wGCV7B8fFLbMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T22:49:25.884353Z"},"content_sha256":"700372247a8ca6be4a451198ce1de0093faf9b0c86abe443e34427b633e8fd6f","schema_version":"1.0","event_id":"sha256:700372247a8ca6be4a451198ce1de0093faf9b0c86abe443e34427b633e8fd6f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CNV32A4TSHBXPUJD7J75CCPNS7/bundle.json","state_url":"https://pith.science/pith/CNV32A4TSHBXPUJD7J75CCPNS7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CNV32A4TSHBXPUJD7J75CCPNS7/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":"2026-07-04T22:49:25Z","links":{"resolver":"https://pith.science/pith/CNV32A4TSHBXPUJD7J75CCPNS7","bundle":"https://pith.science/pith/CNV32A4TSHBXPUJD7J75CCPNS7/bundle.json","state":"https://pith.science/pith/CNV32A4TSHBXPUJD7J75CCPNS7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CNV32A4TSHBXPUJD7J75CCPNS7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CNV32A4TSHBXPUJD7J75CCPNS7","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":"999bbfc2ccd8551d5e2369c3b43890a5e38ab3c8bf382bc41166b96339c52412","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T22:38:43Z","title_canon_sha256":"c8ec324372a3f46f0513f8711d192d1996e66377185ec51fbb2a9537ecc529aa"},"schema_version":"1.0","source":{"id":"1707.09565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.09565","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"1707.09565v1","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09565","created_at":"2026-05-18T00:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"CNV32A4TSHBX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CNV32A4TSHBXPUJD","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CNV32A4T","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:700372247a8ca6be4a451198ce1de0093faf9b0c86abe443e34427b633e8fd6f","target":"graph","created_at":"2026-05-18T00:39:10Z","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":"This paper presents a method for fitting a copula-driven generalized linear mixed models. For added flexibility, the skew-normal copula is adopted for fitting. The correlation matrix of the skew-normal copula is used to capture the dependence structure within units, while the fixed and random effects coefficients are estimated through the mean of the copula. For estimation, a Monte Carlo expectation-maximization algorithm is developed. Simulations are shown alongside a real data example from the Framingham Heart Study.","authors_text":"Arusharka Sen, Kalyan Das, Mohamad Elmasri","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T22:38:43Z","title":"A Skew-Normal Copula-Driven GLMM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09565","kind":"arxiv","version":1},"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:fdc66b0e25783a73d3fee6efbe394ba66d5b14e409840df0e557699de4bee9be","target":"record","created_at":"2026-05-18T00:39:10Z","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":"999bbfc2ccd8551d5e2369c3b43890a5e38ab3c8bf382bc41166b96339c52412","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2017-07-29T22:38:43Z","title_canon_sha256":"c8ec324372a3f46f0513f8711d192d1996e66377185ec51fbb2a9537ecc529aa"},"schema_version":"1.0","source":{"id":"1707.09565","kind":"arxiv","version":1}},"canonical_sha256":"136bbd039391c377d123fa7fd109ed97e2b6598410c8a7f695fde56d673ea0f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"136bbd039391c377d123fa7fd109ed97e2b6598410c8a7f695fde56d673ea0f3","first_computed_at":"2026-05-18T00:39:10.899291Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:10.899291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9dCqjOephFhOzf2I821+avTShCdHzz46cGRp5mUkL7EsI9wB3gf52hOUqtT1412aXM1N8+Bfc7wmP5zMpfBxAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:10.899938Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.09565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fdc66b0e25783a73d3fee6efbe394ba66d5b14e409840df0e557699de4bee9be","sha256:700372247a8ca6be4a451198ce1de0093faf9b0c86abe443e34427b633e8fd6f"],"state_sha256":"1ad96b313de594f65593d91603fed2112bac2697ef86d699b3c1f226520fd179"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aSBogIj3zmo/S4TW9Zib3f4eV8IetHdMbemcSausjcEMo0bAwnqoyNC4CM+5hNC6ZsbeUfBPxl9pLCWts8FYDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T22:49:25.886209Z","bundle_sha256":"e31e87bc63b610063a4d03fa31c1511d6a4062692f456f77af17a0876e7c7177"}}