{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:HXPLDKNIXRIU3DXVUHFZRVWGZI","short_pith_number":"pith:HXPLDKNI","canonical_record":{"source":{"id":"1802.02467","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-07T15:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"05005fc61404acae67f4051b8aac4703c82141a04ee31b75e640bc6aad2fa2a8","abstract_canon_sha256":"e3edff54f2c907dadfad8b347b2dd3ad6cf31cea144678178f320cd563247159"},"schema_version":"1.0"},"canonical_sha256":"3ddeb1a9a8bc514d8ef5a1cb98d6c6ca0b2d11c11aeac312695b08211ed00f0b","source":{"kind":"arxiv","id":"1802.02467","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02467","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02467v2","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02467","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"pith_short_12","alias_value":"HXPLDKNIXRIU","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HXPLDKNIXRIU3DXV","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HXPLDKNI","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:HXPLDKNIXRIU3DXVUHFZRVWGZI","target":"record","payload":{"canonical_record":{"source":{"id":"1802.02467","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-07T15:09:18Z","cross_cats_sorted":[],"title_canon_sha256":"05005fc61404acae67f4051b8aac4703c82141a04ee31b75e640bc6aad2fa2a8","abstract_canon_sha256":"e3edff54f2c907dadfad8b347b2dd3ad6cf31cea144678178f320cd563247159"},"schema_version":"1.0"},"canonical_sha256":"3ddeb1a9a8bc514d8ef5a1cb98d6c6ca0b2d11c11aeac312695b08211ed00f0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:18.223979Z","signature_b64":"WAcI/H2g23Z38z8UCK9kghsb/mXOONrcUPlNiiqnG1MkrF5wzsDYVN2Tr2UVcmTqvpHtzV5gZ4ZVKMCyf+wiCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ddeb1a9a8bc514d8ef5a1cb98d6c6ca0b2d11c11aeac312695b08211ed00f0b","last_reissued_at":"2026-05-18T00:02:18.223485Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:18.223485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.02467","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":"2026-05-18T00:02:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QKQrcsodjWehKcrNqx7+U2/UWTt72LveoSRA8ofJNORhNxGuE/SGp3+q1UeKMsnRp7uFH/CAEEu53xGCDlH1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T02:13:40.254578Z"},"content_sha256":"3a166c161dfb967c9b25a6e3a5ac86e4e0dbc0a268df7481f7ecbcf275b80011","schema_version":"1.0","event_id":"sha256:3a166c161dfb967c9b25a6e3a5ac86e4e0dbc0a268df7481f7ecbcf275b80011"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:HXPLDKNIXRIU3DXVUHFZRVWGZI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Geoffrey J. McLachlan, Sharon X. Lee, Tsung-I Lin","submitted_at":"2018-02-07T15:09:18Z","abstract_excerpt":"Mixtures of factor analyzers (MFA) provide a powerful tool for modelling high-dimensional datasets. In recent years, several generalizations of MFA have been developed where the normality assumption of the factors and/or of the errors was relaxed to allow for skewness in the data. However, due to the form of the adopted component densities, the distribution of the factors/errors in most of these models is typically limited to modelling skewness oncentrated in a single direction. Here, we introduce a more flexible finite mixture of factor analyzers based on the class of scale mixtures of canoni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02467","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":"2026-05-18T00:02:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xUh9AsrnGaMqQkGx5ykqvrSZzvytpxNW2BxUKu0oO7tAPr8cNk8jKIiU6oV7REbouWTgX/XFi+joJEskl7kQBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T02:13:40.255010Z"},"content_sha256":"395d1a3da3c876eb946dc5bff1dcd88f129bb714224436df7af863abeeb3df96","schema_version":"1.0","event_id":"sha256:395d1a3da3c876eb946dc5bff1dcd88f129bb714224436df7af863abeeb3df96"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/bundle.json","state_url":"https://pith.science/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/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":"2026-05-23T02:13:40Z","links":{"resolver":"https://pith.science/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI","bundle":"https://pith.science/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/bundle.json","state":"https://pith.science/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HXPLDKNIXRIU3DXVUHFZRVWGZI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:HXPLDKNIXRIU3DXVUHFZRVWGZI","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":"e3edff54f2c907dadfad8b347b2dd3ad6cf31cea144678178f320cd563247159","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-07T15:09:18Z","title_canon_sha256":"05005fc61404acae67f4051b8aac4703c82141a04ee31b75e640bc6aad2fa2a8"},"schema_version":"1.0","source":{"id":"1802.02467","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02467","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02467v2","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02467","created_at":"2026-05-18T00:02:18Z"},{"alias_kind":"pith_short_12","alias_value":"HXPLDKNIXRIU","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"HXPLDKNIXRIU3DXV","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"HXPLDKNI","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:395d1a3da3c876eb946dc5bff1dcd88f129bb714224436df7af863abeeb3df96","target":"graph","created_at":"2026-05-18T00:02:18Z","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":"Mixtures of factor analyzers (MFA) provide a powerful tool for modelling high-dimensional datasets. In recent years, several generalizations of MFA have been developed where the normality assumption of the factors and/or of the errors was relaxed to allow for skewness in the data. However, due to the form of the adopted component densities, the distribution of the factors/errors in most of these models is typically limited to modelling skewness oncentrated in a single direction. Here, we introduce a more flexible finite mixture of factor analyzers based on the class of scale mixtures of canoni","authors_text":"Geoffrey J. McLachlan, Sharon X. Lee, Tsung-I Lin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-07T15:09:18Z","title":"Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02467","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":"sha256:3a166c161dfb967c9b25a6e3a5ac86e4e0dbc0a268df7481f7ecbcf275b80011","target":"record","created_at":"2026-05-18T00:02:18Z","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":"e3edff54f2c907dadfad8b347b2dd3ad6cf31cea144678178f320cd563247159","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-07T15:09:18Z","title_canon_sha256":"05005fc61404acae67f4051b8aac4703c82141a04ee31b75e640bc6aad2fa2a8"},"schema_version":"1.0","source":{"id":"1802.02467","kind":"arxiv","version":2}},"canonical_sha256":"3ddeb1a9a8bc514d8ef5a1cb98d6c6ca0b2d11c11aeac312695b08211ed00f0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ddeb1a9a8bc514d8ef5a1cb98d6c6ca0b2d11c11aeac312695b08211ed00f0b","first_computed_at":"2026-05-18T00:02:18.223485Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:18.223485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WAcI/H2g23Z38z8UCK9kghsb/mXOONrcUPlNiiqnG1MkrF5wzsDYVN2Tr2UVcmTqvpHtzV5gZ4ZVKMCyf+wiCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:18.223979Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.02467","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a166c161dfb967c9b25a6e3a5ac86e4e0dbc0a268df7481f7ecbcf275b80011","sha256:395d1a3da3c876eb946dc5bff1dcd88f129bb714224436df7af863abeeb3df96"],"state_sha256":"d1daaebcfdad1a386f833582db447ec7890709a6c524c3d036103e8e231ab09e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s6nyicBsvgV14rZDYqIhlTCWmMra+yutjzqJrOT5kX9cV2cot3YM4Z2e4OyBM94Ilg1Wv+UkPRyjqpEKIdYZBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T02:13:40.258597Z","bundle_sha256":"76026530dfa592a8b8b0082ed14ff5fae3b287a124c7c6bcb10f49584bd7d773"}}