{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TVDIYROQUPDMHBVDV2CYPMVR2Q","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":"3781b05375379c983f01ff843f723ac3da24e26f6a19900e8fdb440e39dab077","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-05-16T15:14:21Z","title_canon_sha256":"6ebb94b873734a557dfa446aa6be0a130f624b5c44af1703e74863a546bb7446"},"schema_version":"1.0","source":{"id":"1805.06364","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.06364","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"1805.06364v3","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06364","created_at":"2026-05-17T23:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"TVDIYROQUPDM","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"TVDIYROQUPDMHBVD","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"TVDIYROQ","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:6b7e65a80a11dc3a6ea9f11057d19b080bdcd685df4b2f2402fd91b5caa36180","target":"graph","created_at":"2026-05-17T23:51:26Z","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":"In real applications of the linear model, the explanatory variables are very often naturally grouped, the most common example being the multivariate variance analysis. In the present paper, a quantile model with structure group is considered, the number of groups can diverge with sample size. We introduce and study the adaptive elastic-net group estimator, for improving the parameter estimation accuracy. This method allows automatic selection, with a probability converging to one, of significant groups and further the non zero parameter estimators are asymptotically normal. The convergence rat","authors_text":"Gabriela Ciuperca","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-05-16T15:14:21Z","title":"Adaptive elastic-net selection in a quantile model with diverging number of variable groups"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06364","kind":"arxiv","version":3},"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":"sha256:90377cbea55b0f83d937b8c8dd7757f293f1c7bc768970dc3e592bd1b079e10f","target":"record","created_at":"2026-05-17T23:51:26Z","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":"3781b05375379c983f01ff843f723ac3da24e26f6a19900e8fdb440e39dab077","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-05-16T15:14:21Z","title_canon_sha256":"6ebb94b873734a557dfa446aa6be0a130f624b5c44af1703e74863a546bb7446"},"schema_version":"1.0","source":{"id":"1805.06364","kind":"arxiv","version":3}},"canonical_sha256":"9d468c45d0a3c6c386a3ae8587b2b1d41c42fa16154258cfeb6105caa01c0a3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d468c45d0a3c6c386a3ae8587b2b1d41c42fa16154258cfeb6105caa01c0a3f","first_computed_at":"2026-05-17T23:51:26.090326Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:26.090326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YTzB7WZGoaASjgIflXJ3Eb7CzlYWXARt5fFD1bk6jLFhbGCtISvQmXeTXSY1RrOGIpd0qR7iBYsDlN38RxQiDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:26.090814Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.06364","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90377cbea55b0f83d937b8c8dd7757f293f1c7bc768970dc3e592bd1b079e10f","sha256:6b7e65a80a11dc3a6ea9f11057d19b080bdcd685df4b2f2402fd91b5caa36180"],"state_sha256":"37a54a0074506eca829fa4a550f5318980b5fc44e57542ce93e80c491c9c3d95"}