{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TDOCSYQGJWOBIEJI2GPYB6OZPC","short_pith_number":"pith:TDOCSYQG","schema_version":"1.0","canonical_sha256":"98dc2962064d9c141128d19f80f9d978ad8903339274cfd428a9ffc1f70d44bb","source":{"kind":"arxiv","id":"2606.20406","version":1},"attestation_state":"computed","paper":{"title":"Flexible modeling of bimodal distributions via skewed-$t$ mixtures","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Flavio Santi, Marco Bee","submitted_at":"2026-06-18T15:57:20Z","abstract_excerpt":"We propose a mixture of location-scale skewed-$t$ distributions to fit bimodal, skewed and heavy-tailed data. In particular, the mixture is based on the skewed-$t$ distribution by Fern\\'andez and Steel (1998), so that the model-building procedure can be easily extended to mixtures of other symmetric distributions. After studying the properties of the mixture, we develop a maximum likelihood estimation approach via the EM algorithm and a likelihood ratio test of the null hypothesis of no skewness in any given component. A simulation-based comparison to a recently proposed mixture of g-and-h dis"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.20406","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-18T15:57:20Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"5acb4c85e5472309172232b95cae320d81400eee5c8a28304521033fe078b57a","abstract_canon_sha256":"9a3abe9a9b59b3fa1264c12e6bf057f032b34d0a8908f13e3ed241e0f7dfba64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:13:11.742185Z","signature_b64":"uhMn9E5CgGzaY8PYolmyaEDoCSxZVy71oUf2XuQlLKwRCvaIqJq1QXwS3xO8f49fGXk3UcLgb9HuKtEr0qNyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98dc2962064d9c141128d19f80f9d978ad8903339274cfd428a9ffc1f70d44bb","last_reissued_at":"2026-06-19T16:13:11.741810Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:13:11.741810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Flexible modeling of bimodal distributions via skewed-$t$ mixtures","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Flavio Santi, Marco Bee","submitted_at":"2026-06-18T15:57:20Z","abstract_excerpt":"We propose a mixture of location-scale skewed-$t$ distributions to fit bimodal, skewed and heavy-tailed data. In particular, the mixture is based on the skewed-$t$ distribution by Fern\\'andez and Steel (1998), so that the model-building procedure can be easily extended to mixtures of other symmetric distributions. After studying the properties of the mixture, we develop a maximum likelihood estimation approach via the EM algorithm and a likelihood ratio test of the null hypothesis of no skewness in any given component. A simulation-based comparison to a recently proposed mixture of g-and-h dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20406","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.20406/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.20406","created_at":"2026-06-19T16:13:11.741871+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20406v1","created_at":"2026-06-19T16:13:11.741871+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20406","created_at":"2026-06-19T16:13:11.741871+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDOCSYQGJWOB","created_at":"2026-06-19T16:13:11.741871+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDOCSYQGJWOBIEJI","created_at":"2026-06-19T16:13:11.741871+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDOCSYQG","created_at":"2026-06-19T16:13:11.741871+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC","json":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC.json","graph_json":"https://pith.science/api/pith-number/TDOCSYQGJWOBIEJI2GPYB6OZPC/graph.json","events_json":"https://pith.science/api/pith-number/TDOCSYQGJWOBIEJI2GPYB6OZPC/events.json","paper":"https://pith.science/paper/TDOCSYQG"},"agent_actions":{"view_html":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC","download_json":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC.json","view_paper":"https://pith.science/paper/TDOCSYQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20406&json=true","fetch_graph":"https://pith.science/api/pith-number/TDOCSYQGJWOBIEJI2GPYB6OZPC/graph.json","fetch_events":"https://pith.science/api/pith-number/TDOCSYQGJWOBIEJI2GPYB6OZPC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC/action/storage_attestation","attest_author":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC/action/author_attestation","sign_citation":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC/action/citation_signature","submit_replication":"https://pith.science/pith/TDOCSYQGJWOBIEJI2GPYB6OZPC/action/replication_record"}},"created_at":"2026-06-19T16:13:11.741871+00:00","updated_at":"2026-06-19T16:13:11.741871+00:00"}