{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:HAOE2S3XJYZNRWX56245PIF55R","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":"da31a5aaa13cb0d3ecd0832a155ed802dd0f98a112c170c3ca82061962be863c","cross_cats_sorted":["stat.OT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-02T19:12:05Z","title_canon_sha256":"a6af4313f10c4c6d948ebd69c279c1330bf1a1da3861cad868b4e9e4372fb56c"},"schema_version":"1.0","source":{"id":"1511.00634","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.00634","created_at":"2026-05-18T00:09:10Z"},{"alias_kind":"arxiv_version","alias_value":"1511.00634v4","created_at":"2026-05-18T00:09:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.00634","created_at":"2026-05-18T00:09:10Z"},{"alias_kind":"pith_short_12","alias_value":"HAOE2S3XJYZN","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"HAOE2S3XJYZNRWX5","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"HAOE2S3X","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:fa605277d6110c4c8b7c4bf104a1c9b3b5e9e783962be9adf5561dc1d71a957d","target":"graph","created_at":"2026-05-18T00:09:10Z","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":"Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdis","authors_text":"Hadeel S. Klakattawi, Keming Yu, Veronica Vinciotti","cross_cats":["stat.OT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-02T19:12:05Z","title":"A Simple and Adaptive Dispersion Regression Model for Count Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.00634","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":"sha256:0a5d038e5a541d973a8283e62e65fc5cf848095da77b60aa26b98293f7b05777","target":"record","created_at":"2026-05-18T00:09:10Z","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":"da31a5aaa13cb0d3ecd0832a155ed802dd0f98a112c170c3ca82061962be863c","cross_cats_sorted":["stat.OT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-11-02T19:12:05Z","title_canon_sha256":"a6af4313f10c4c6d948ebd69c279c1330bf1a1da3861cad868b4e9e4372fb56c"},"schema_version":"1.0","source":{"id":"1511.00634","kind":"arxiv","version":4}},"canonical_sha256":"381c4d4b774e32d8dafdf6b9d7a0bdec66cf0a15dfdbb23638ec093251034584","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"381c4d4b774e32d8dafdf6b9d7a0bdec66cf0a15dfdbb23638ec093251034584","first_computed_at":"2026-05-18T00:09:10.471597Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:10.471597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mulaA8zekmHavFt8dEvnHE23W5T4dhVmqKIIXHVGPnAsUz6+NmC4YbEXwirYODczqxpQ2XKr5mav8N8zV88pCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:10.472209Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.00634","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a5d038e5a541d973a8283e62e65fc5cf848095da77b60aa26b98293f7b05777","sha256:fa605277d6110c4c8b7c4bf104a1c9b3b5e9e783962be9adf5561dc1d71a957d"],"state_sha256":"d2565f53ac5a7a8f54cf1cbcfe880ace7fc7346bd1f9c30f24cbff8b64d87b37"}