{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GNV7IEI7LGCMHIN4LGENNI22YL","short_pith_number":"pith:GNV7IEI7","schema_version":"1.0","canonical_sha256":"336bf4111f5984c3a1bc5988d6a35ac2ce67fa6df73ea556bd2928b5f41e74e6","source":{"kind":"arxiv","id":"1602.04910","version":1},"attestation_state":"computed","paper":{"title":"Bayesian generalized fused lasso modeling via NEG distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Kaito Shimamura, Masao Ueki, Sadanori Konishi, Shuichi Kawano","submitted_at":"2016-02-16T05:20:14Z","abstract_excerpt":"The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso by using a flexible regularization term. We also propose a sparse fused algorithm to produce exact sparse solutions. Si"},"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":"1602.04910","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-16T05:20:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fe9b20d9b67e2df2c4a0c400c2184fc16a5871769de4aed12a52d25e83b4a09c","abstract_canon_sha256":"7c96a8008ad5c0fad56a13e2839b5417dd1182156ce7e927a587d71dda9bbf74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:50.090276Z","signature_b64":"/Lu5hCyMj4M053+XBa8yD0H504mCgcaEYr/jeeQzrghi6QcxXNGtuYkD6KAq3AW17aBqqXAv3850de28zLVtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"336bf4111f5984c3a1bc5988d6a35ac2ce67fa6df73ea556bd2928b5f41e74e6","last_reissued_at":"2026-05-17T23:40:50.089625Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:50.089625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian generalized fused lasso modeling via NEG distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Kaito Shimamura, Masao Ueki, Sadanori Konishi, Shuichi Kawano","submitted_at":"2016-02-16T05:20:14Z","abstract_excerpt":"The fused lasso penalizes a loss function by the $L_1$ norm for both the regression coefficients and their successive differences to encourage sparsity of both. In this paper, we propose a Bayesian generalized fused lasso modeling based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is assumed into the difference of successive regression coefficients. The proposed method enables us to construct a more versatile sparse model than the ordinary fused lasso by using a flexible regularization term. We also propose a sparse fused algorithm to produce exact sparse solutions. Si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04910","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1602.04910","created_at":"2026-05-17T23:40:50.089736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.04910v1","created_at":"2026-05-17T23:40:50.089736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04910","created_at":"2026-05-17T23:40:50.089736+00:00"},{"alias_kind":"pith_short_12","alias_value":"GNV7IEI7LGCM","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GNV7IEI7LGCMHIN4","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GNV7IEI7","created_at":"2026-05-18T12:30:19.053100+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/GNV7IEI7LGCMHIN4LGENNI22YL","json":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL.json","graph_json":"https://pith.science/api/pith-number/GNV7IEI7LGCMHIN4LGENNI22YL/graph.json","events_json":"https://pith.science/api/pith-number/GNV7IEI7LGCMHIN4LGENNI22YL/events.json","paper":"https://pith.science/paper/GNV7IEI7"},"agent_actions":{"view_html":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL","download_json":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL.json","view_paper":"https://pith.science/paper/GNV7IEI7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.04910&json=true","fetch_graph":"https://pith.science/api/pith-number/GNV7IEI7LGCMHIN4LGENNI22YL/graph.json","fetch_events":"https://pith.science/api/pith-number/GNV7IEI7LGCMHIN4LGENNI22YL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL/action/storage_attestation","attest_author":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL/action/author_attestation","sign_citation":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL/action/citation_signature","submit_replication":"https://pith.science/pith/GNV7IEI7LGCMHIN4LGENNI22YL/action/replication_record"}},"created_at":"2026-05-17T23:40:50.089736+00:00","updated_at":"2026-05-17T23:40:50.089736+00:00"}