{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:Z5TEYGMMAXUP7ZTDFPHA5OEO5Z","short_pith_number":"pith:Z5TEYGMM","canonical_record":{"source":{"id":"1610.01526","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-05T16:57:31Z","cross_cats_sorted":[],"title_canon_sha256":"149844a19453abb55bcaa461387c191565503df556b3a3c2812c70186cd33866","abstract_canon_sha256":"96b97c0efb9656b506db1d67ed2b2bacc621ec0d9505504d8a6f7543a9b25b25"},"schema_version":"1.0"},"canonical_sha256":"cf664c198c05e8ffe6632bce0eb88eee7797a26fd47d0aaf9be41a227a27d0b3","source":{"kind":"arxiv","id":"1610.01526","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.01526","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"arxiv_version","alias_value":"1610.01526v2","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.01526","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"pith_short_12","alias_value":"Z5TEYGMMAXUP","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"Z5TEYGMMAXUP7ZTD","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"Z5TEYGMM","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:Z5TEYGMMAXUP7ZTDFPHA5OEO5Z","target":"record","payload":{"canonical_record":{"source":{"id":"1610.01526","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-05T16:57:31Z","cross_cats_sorted":[],"title_canon_sha256":"149844a19453abb55bcaa461387c191565503df556b3a3c2812c70186cd33866","abstract_canon_sha256":"96b97c0efb9656b506db1d67ed2b2bacc621ec0d9505504d8a6f7543a9b25b25"},"schema_version":"1.0"},"canonical_sha256":"cf664c198c05e8ffe6632bce0eb88eee7797a26fd47d0aaf9be41a227a27d0b3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:13.515421Z","signature_b64":"GZ8gn6AjkSNtp+jBw/95tT4EcYEaXnTcMKvd5/Ww4XNe6Egcr6MhWkeIFwAeFhMRRgcBxpIrJutHYnklw91VAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf664c198c05e8ffe6632bce0eb88eee7797a26fd47d0aaf9be41a227a27d0b3","last_reissued_at":"2026-05-18T00:50:13.514957Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:13.514957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.01526","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:50:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"trG9RUNQba5rF26N+4ivyRTJVge/TVPzUbMniczZ88HBB42S90gnIo8AjxSgpspd4c/laKza29RzELhghR3LAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:18:23.155673Z"},"content_sha256":"91ba3642455dc86ec8b163791efd9fcf14c00d978f6ebe84c5b7569c487960ca","schema_version":"1.0","event_id":"sha256:91ba3642455dc86ec8b163791efd9fcf14c00d978f6ebe84c5b7569c487960ca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:Z5TEYGMMAXUP7ZTDFPHA5OEO5Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Marginally Interpretable Generalized Linear Mixed Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jeffrey J. Gory, Peter F. Craigmile, Steven N. MacEachern","submitted_at":"2016-10-05T16:57:31Z","abstract_excerpt":"Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. The second approach overcomes the deficiencies of the first, but leads to parameter estimates that must be interpreted conditional on the la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.01526","kind":"arxiv","version":2},"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":"2026-05-18T00:50:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ealN/EgLwPB8NPF1Gg4VrKxgmFVDXbas8Qupc8jBHYynpGbnfd/TS6zo9ytOFshwtlqNMBr1w0U0QPOZBrOADQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:18:23.156031Z"},"content_sha256":"adf21a0d0259c3cc7fd92cc441a27129887aa5d3765633c33b44bc0a76fc21e4","schema_version":"1.0","event_id":"sha256:adf21a0d0259c3cc7fd92cc441a27129887aa5d3765633c33b44bc0a76fc21e4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/bundle.json","state_url":"https://pith.science/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/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":"2026-06-02T12:18:23Z","links":{"resolver":"https://pith.science/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z","bundle":"https://pith.science/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/bundle.json","state":"https://pith.science/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z5TEYGMMAXUP7ZTDFPHA5OEO5Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:Z5TEYGMMAXUP7ZTDFPHA5OEO5Z","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":"96b97c0efb9656b506db1d67ed2b2bacc621ec0d9505504d8a6f7543a9b25b25","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-05T16:57:31Z","title_canon_sha256":"149844a19453abb55bcaa461387c191565503df556b3a3c2812c70186cd33866"},"schema_version":"1.0","source":{"id":"1610.01526","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.01526","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"arxiv_version","alias_value":"1610.01526v2","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.01526","created_at":"2026-05-18T00:50:13Z"},{"alias_kind":"pith_short_12","alias_value":"Z5TEYGMMAXUP","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"Z5TEYGMMAXUP7ZTD","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"Z5TEYGMM","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:adf21a0d0259c3cc7fd92cc441a27129887aa5d3765633c33b44bc0a76fc21e4","target":"graph","created_at":"2026-05-18T00:50:13Z","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":"Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. The second approach overcomes the deficiencies of the first, but leads to parameter estimates that must be interpreted conditional on the la","authors_text":"Jeffrey J. Gory, Peter F. Craigmile, Steven N. MacEachern","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-05T16:57:31Z","title":"Marginally Interpretable Generalized Linear Mixed Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.01526","kind":"arxiv","version":2},"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":"sha256:91ba3642455dc86ec8b163791efd9fcf14c00d978f6ebe84c5b7569c487960ca","target":"record","created_at":"2026-05-18T00:50:13Z","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":"96b97c0efb9656b506db1d67ed2b2bacc621ec0d9505504d8a6f7543a9b25b25","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-05T16:57:31Z","title_canon_sha256":"149844a19453abb55bcaa461387c191565503df556b3a3c2812c70186cd33866"},"schema_version":"1.0","source":{"id":"1610.01526","kind":"arxiv","version":2}},"canonical_sha256":"cf664c198c05e8ffe6632bce0eb88eee7797a26fd47d0aaf9be41a227a27d0b3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf664c198c05e8ffe6632bce0eb88eee7797a26fd47d0aaf9be41a227a27d0b3","first_computed_at":"2026-05-18T00:50:13.514957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:13.514957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GZ8gn6AjkSNtp+jBw/95tT4EcYEaXnTcMKvd5/Ww4XNe6Egcr6MhWkeIFwAeFhMRRgcBxpIrJutHYnklw91VAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:13.515421Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.01526","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:91ba3642455dc86ec8b163791efd9fcf14c00d978f6ebe84c5b7569c487960ca","sha256:adf21a0d0259c3cc7fd92cc441a27129887aa5d3765633c33b44bc0a76fc21e4"],"state_sha256":"93210aaa2450683c27665890fad925082358726a10daffbb32f53ecbc6901b3e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l7d3CHPqraQ1JB7P7r7oxn3OI840SLI/6kJR267YhQO6z6yj/dQVmZoGWTnCAlw7TXSLJex4X+FuNfD12pqqBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T12:18:23.158727Z","bundle_sha256":"780ba6147526f2dd448fe1e22088129bd289ac5b3686621e9899c24c02942d9c"}}