{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:7AMWKWIFSX5CPM4AJBB5MI44DJ","short_pith_number":"pith:7AMWKWIF","canonical_record":{"source":{"id":"1612.03974","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-12T23:52:59Z","cross_cats_sorted":["math.NA"],"title_canon_sha256":"bd7fef91381ad78e829455b1019638644701e7fdfb5be5ed3df499319a799e89","abstract_canon_sha256":"a29c88b8a483338d709290a1abfe54f8dd149d26ff521f90f95fcf0843658e49"},"schema_version":"1.0"},"canonical_sha256":"f81965590595fa27b3804843d6239c1a623e5bfef9573df1fc19e5758f081a6b","source":{"kind":"arxiv","id":"1612.03974","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03974","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03974v2","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03974","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"pith_short_12","alias_value":"7AMWKWIFSX5C","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7AMWKWIFSX5CPM4A","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7AMWKWIF","created_at":"2026-05-18T12:30:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:7AMWKWIFSX5CPM4AJBB5MI44DJ","target":"record","payload":{"canonical_record":{"source":{"id":"1612.03974","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-12T23:52:59Z","cross_cats_sorted":["math.NA"],"title_canon_sha256":"bd7fef91381ad78e829455b1019638644701e7fdfb5be5ed3df499319a799e89","abstract_canon_sha256":"a29c88b8a483338d709290a1abfe54f8dd149d26ff521f90f95fcf0843658e49"},"schema_version":"1.0"},"canonical_sha256":"f81965590595fa27b3804843d6239c1a623e5bfef9573df1fc19e5758f081a6b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:21.938876Z","signature_b64":"lYjLDJYtsNAAOPsazl8axgr6zXLvk9QqfhRr1Yur+9DbTUQpFdu9WW6xAC6LYK5qyX5COacLwohfomZQhrLbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f81965590595fa27b3804843d6239c1a623e5bfef9573df1fc19e5758f081a6b","last_reissued_at":"2026-05-18T00:27:21.938180Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:21.938180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.03974","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":"2026-05-18T00:27:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1wlOKnrlQT+GOR8uVlcge6CkF/3Yn6KQYWzDwT01BfJAUQE0gpjNjTICpShooNX8HGRsWlZN9Ofz0CcDISdZDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T13:52:35.820142Z"},"content_sha256":"af0a756e3eb9ad6e7e41bbb6e4537d2da7dfeb42631133a2b20a95a368170d63","schema_version":"1.0","event_id":"sha256:af0a756e3eb9ad6e7e41bbb6e4537d2da7dfeb42631133a2b20a95a368170d63"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:7AMWKWIFSX5CPM4AJBB5MI44DJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A self-calibrating method for heavy tailed data modelling. Application in neuroscience and finance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.NA"],"primary_cat":"stat.ME","authors_text":"Mamadou Mboup, Marie Kratz, Nehla Debbabi","submitted_at":"2016-12-12T23:52:59Z","abstract_excerpt":"Modelling non-homogeneous and multi-component data is a problem that challenges scientific researchers in several fields. In general, it is not possible to find a simple and closed form probabilistic model to describe such data. That is why one often resorts to non-parametric approaches. However, when the multiple components are separable, parametric modelling becomes again tractable. In this study, we propose a self-calibrating method to model multi-component data that exhibit heavy tails. We introduce a three-component hybrid distribution: a Gaussian distribution is linked to a Generalized P"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03974","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":"2026-05-18T00:27:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ER28zjOjUVQxLaJp7BNKXJcZQg0WZril+6WdxiOy1WcHMEgusTk8ueDy67zyYZEOlnPORLqoBpK/77mLVdv9AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T13:52:35.820510Z"},"content_sha256":"e07ed4ece0c11a642e1ce5d9d456e97d15fa61b031ad7366e1a914fce4080bc7","schema_version":"1.0","event_id":"sha256:e07ed4ece0c11a642e1ce5d9d456e97d15fa61b031ad7366e1a914fce4080bc7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/bundle.json","state_url":"https://pith.science/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/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":"2026-06-23T13:52:35Z","links":{"resolver":"https://pith.science/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ","bundle":"https://pith.science/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/bundle.json","state":"https://pith.science/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7AMWKWIFSX5CPM4AJBB5MI44DJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:7AMWKWIFSX5CPM4AJBB5MI44DJ","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":"a29c88b8a483338d709290a1abfe54f8dd149d26ff521f90f95fcf0843658e49","cross_cats_sorted":["math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-12T23:52:59Z","title_canon_sha256":"bd7fef91381ad78e829455b1019638644701e7fdfb5be5ed3df499319a799e89"},"schema_version":"1.0","source":{"id":"1612.03974","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.03974","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"arxiv_version","alias_value":"1612.03974v2","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.03974","created_at":"2026-05-18T00:27:21Z"},{"alias_kind":"pith_short_12","alias_value":"7AMWKWIFSX5C","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7AMWKWIFSX5CPM4A","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7AMWKWIF","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:e07ed4ece0c11a642e1ce5d9d456e97d15fa61b031ad7366e1a914fce4080bc7","target":"graph","created_at":"2026-05-18T00:27:21Z","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":"Modelling non-homogeneous and multi-component data is a problem that challenges scientific researchers in several fields. In general, it is not possible to find a simple and closed form probabilistic model to describe such data. That is why one often resorts to non-parametric approaches. However, when the multiple components are separable, parametric modelling becomes again tractable. In this study, we propose a self-calibrating method to model multi-component data that exhibit heavy tails. We introduce a three-component hybrid distribution: a Gaussian distribution is linked to a Generalized P","authors_text":"Mamadou Mboup, Marie Kratz, Nehla Debbabi","cross_cats":["math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-12T23:52:59Z","title":"A self-calibrating method for heavy tailed data modelling. Application in neuroscience and finance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.03974","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:af0a756e3eb9ad6e7e41bbb6e4537d2da7dfeb42631133a2b20a95a368170d63","target":"record","created_at":"2026-05-18T00:27:21Z","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":"a29c88b8a483338d709290a1abfe54f8dd149d26ff521f90f95fcf0843658e49","cross_cats_sorted":["math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-12-12T23:52:59Z","title_canon_sha256":"bd7fef91381ad78e829455b1019638644701e7fdfb5be5ed3df499319a799e89"},"schema_version":"1.0","source":{"id":"1612.03974","kind":"arxiv","version":2}},"canonical_sha256":"f81965590595fa27b3804843d6239c1a623e5bfef9573df1fc19e5758f081a6b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f81965590595fa27b3804843d6239c1a623e5bfef9573df1fc19e5758f081a6b","first_computed_at":"2026-05-18T00:27:21.938180Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:21.938180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lYjLDJYtsNAAOPsazl8axgr6zXLvk9QqfhRr1Yur+9DbTUQpFdu9WW6xAC6LYK5qyX5COacLwohfomZQhrLbDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:21.938876Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.03974","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af0a756e3eb9ad6e7e41bbb6e4537d2da7dfeb42631133a2b20a95a368170d63","sha256:e07ed4ece0c11a642e1ce5d9d456e97d15fa61b031ad7366e1a914fce4080bc7"],"state_sha256":"01828be113e8c46dbd5efb87ec8453a4e7d3cec1da88f5d41ab67bf4efd181af"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l/ysLPqmYtfoMZkKPFOnqeHbr52ft8pJbz1o1qyBbOs/bKpDrc3sF0w/7UB/nRDQWV+/myt6D1JhbAhXzdXOAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T13:52:35.822421Z","bundle_sha256":"c43eff846d5677e155d1ba2228f42a6a7a51d0dff04690873bc9b545921758df"}}