{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:F5MKLRGLCAYFFS5K2GAS7IMQ35","short_pith_number":"pith:F5MKLRGL","canonical_record":{"source":{"id":"1506.00878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-02T13:38:22Z","cross_cats_sorted":[],"title_canon_sha256":"4b842db28a13b986df13505e4cf4a3652fd43a746b1768f2eb8848ac66ee5c60","abstract_canon_sha256":"99af20a4f97d603e0fba5226ad23ba498b18395ad066ee5b3fd130c7787d70d4"},"schema_version":"1.0"},"canonical_sha256":"2f58a5c4cb103052cbaad1812fa190df7b42b955d8c985a7ac12dab55b462861","source":{"kind":"arxiv","id":"1506.00878","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.00878","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"arxiv_version","alias_value":"1506.00878v1","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.00878","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"pith_short_12","alias_value":"F5MKLRGLCAYF","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F5MKLRGLCAYFFS5K","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F5MKLRGL","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:F5MKLRGLCAYFFS5K2GAS7IMQ35","target":"record","payload":{"canonical_record":{"source":{"id":"1506.00878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-02T13:38:22Z","cross_cats_sorted":[],"title_canon_sha256":"4b842db28a13b986df13505e4cf4a3652fd43a746b1768f2eb8848ac66ee5c60","abstract_canon_sha256":"99af20a4f97d603e0fba5226ad23ba498b18395ad066ee5b3fd130c7787d70d4"},"schema_version":"1.0"},"canonical_sha256":"2f58a5c4cb103052cbaad1812fa190df7b42b955d8c985a7ac12dab55b462861","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:59:34.189592Z","signature_b64":"DKYlw+wZBZpLXgREW78/m9NkquA25afs8HPHG/LdsLAgG9coRV9kx5bXZHM9jF11HQMd6HzLf7dpc47JyefpCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f58a5c4cb103052cbaad1812fa190df7b42b955d8c985a7ac12dab55b462861","last_reissued_at":"2026-05-18T01:59:34.189042Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:59:34.189042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.00878","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":"2026-05-18T01:59:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NV77kpCdqHbeutc3qBuCPXkJuVkHUG+S5PHxLL9fa9j2wVhbVD6NmlIPWdfaruQPLkhiZjBsVRRdWwVj3j0uDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:14:43.025593Z"},"content_sha256":"36463cb4e17568f4ae576ffb5e4bd7d26132ad986a8a1afbc0e4fa34ad54e7c9","schema_version":"1.0","event_id":"sha256:36463cb4e17568f4ae576ffb5e4bd7d26132ad986a8a1afbc0e4fa34ad54e7c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:F5MKLRGLCAYFFS5K2GAS7IMQ35","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Maximum Approximated Likelihood Inference for Tukey's g-and-h Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ganggang Xu, Marc G. Genton","submitted_at":"2015-06-02T13:38:22Z","abstract_excerpt":"Tukey's $g$-and-$h$ distribution has been a powerful tool for data exploration and modeling since its introduction. However, two long standing challenges associated with this distribution family have remained unsolved until this day: how to find an optimal estimation procedure and how to make valid statistical inference on unknown parameters. To overcome these two challenges, a computationally efficient estimation procedure based on maximizing an approximated likelihood function of the Tukey's $g$-and-$h$ distribution is proposed and is shown to have the same estimation efficiency as the maxim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.00878","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":"2026-05-18T01:59:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GlJPyoTQFrHswfI+qYZtSLpQE3teROkWVdxCPjsRKBE6dK3LqDm16oCh+hTEHd9UOyA89JX+WMDHSc+ImJ1KBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:14:43.026019Z"},"content_sha256":"9f7c72388b9548c2c203c7ba504a1da9669724b6aa793be2a1b11d94c01733b1","schema_version":"1.0","event_id":"sha256:9f7c72388b9548c2c203c7ba504a1da9669724b6aa793be2a1b11d94c01733b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/bundle.json","state_url":"https://pith.science/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/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":"2026-05-25T22:14:43Z","links":{"resolver":"https://pith.science/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35","bundle":"https://pith.science/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/bundle.json","state":"https://pith.science/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F5MKLRGLCAYFFS5K2GAS7IMQ35/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:F5MKLRGLCAYFFS5K2GAS7IMQ35","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":"99af20a4f97d603e0fba5226ad23ba498b18395ad066ee5b3fd130c7787d70d4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-02T13:38:22Z","title_canon_sha256":"4b842db28a13b986df13505e4cf4a3652fd43a746b1768f2eb8848ac66ee5c60"},"schema_version":"1.0","source":{"id":"1506.00878","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.00878","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"arxiv_version","alias_value":"1506.00878v1","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.00878","created_at":"2026-05-18T01:59:34Z"},{"alias_kind":"pith_short_12","alias_value":"F5MKLRGLCAYF","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"F5MKLRGLCAYFFS5K","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"F5MKLRGL","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:9f7c72388b9548c2c203c7ba504a1da9669724b6aa793be2a1b11d94c01733b1","target":"graph","created_at":"2026-05-18T01:59:34Z","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":"Tukey's $g$-and-$h$ distribution has been a powerful tool for data exploration and modeling since its introduction. However, two long standing challenges associated with this distribution family have remained unsolved until this day: how to find an optimal estimation procedure and how to make valid statistical inference on unknown parameters. To overcome these two challenges, a computationally efficient estimation procedure based on maximizing an approximated likelihood function of the Tukey's $g$-and-$h$ distribution is proposed and is shown to have the same estimation efficiency as the maxim","authors_text":"Ganggang Xu, Marc G. Genton","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-02T13:38:22Z","title":"Efficient Maximum Approximated Likelihood Inference for Tukey's g-and-h Distribution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.00878","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":"sha256:36463cb4e17568f4ae576ffb5e4bd7d26132ad986a8a1afbc0e4fa34ad54e7c9","target":"record","created_at":"2026-05-18T01:59:34Z","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":"99af20a4f97d603e0fba5226ad23ba498b18395ad066ee5b3fd130c7787d70d4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-02T13:38:22Z","title_canon_sha256":"4b842db28a13b986df13505e4cf4a3652fd43a746b1768f2eb8848ac66ee5c60"},"schema_version":"1.0","source":{"id":"1506.00878","kind":"arxiv","version":1}},"canonical_sha256":"2f58a5c4cb103052cbaad1812fa190df7b42b955d8c985a7ac12dab55b462861","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f58a5c4cb103052cbaad1812fa190df7b42b955d8c985a7ac12dab55b462861","first_computed_at":"2026-05-18T01:59:34.189042Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:59:34.189042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DKYlw+wZBZpLXgREW78/m9NkquA25afs8HPHG/LdsLAgG9coRV9kx5bXZHM9jF11HQMd6HzLf7dpc47JyefpCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:59:34.189592Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.00878","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36463cb4e17568f4ae576ffb5e4bd7d26132ad986a8a1afbc0e4fa34ad54e7c9","sha256:9f7c72388b9548c2c203c7ba504a1da9669724b6aa793be2a1b11d94c01733b1"],"state_sha256":"d5b98ffbd9eda343f53f0ee245de60d3fff7308311909ef1f416c387a17be5a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wjFI55dgPHSMaJt23oV5j771v9pfDroKaiXf/3ZXd2DyQ2r16ze0H4Z0pFemE2I5x24R4heFHtsSpA+frk/WAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:14:43.029195Z","bundle_sha256":"b0076e503547a469ba05c8c6033ca97d8d03bc4afb728ece878887c3ecc179de"}}