{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:UV42HYFEXXVP3ONO65JCZDDYWT","short_pith_number":"pith:UV42HYFE","schema_version":"1.0","canonical_sha256":"a579a3e0a4bdeafdb9aef7522c8c78b4dacfa1c6eb2ef9454764da836507a0f1","source":{"kind":"arxiv","id":"1209.2437","version":1},"attestation_state":"computed","paper":{"title":"TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Han Liu, Lie Wang","submitted_at":"2012-09-11T20:53:45Z","abstract_excerpt":"We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts to choose the tuning parameter in finite sample settings. Computationally, our procedure is significantly faster than existing methods due to its tuning-insensitive property. Theoretically, the obtained estimator is simultaneously minimax optimal for precision matrix estimation under different norms. Empirically, we illustrate the advantages of our method using thorough simulated and real example"},"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":"1209.2437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-09-11T20:53:45Z","cross_cats_sorted":[],"title_canon_sha256":"f4216bd914f53173fbd338b55ba3cd3363e15f85fcadb054327c6d9e8e6ab5da","abstract_canon_sha256":"dde683134f80176080a18426adac37e2b8ed000038b31b01bd148e05917f8bf5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:45:43.172148Z","signature_b64":"iqWXFuhoxLXBZzn2nPcdND9zIxzVLmjy4YTNDTv1h1JMMf3z2A3LTcfOGJNoF33bSRjzUWb7FsuLLyWYyDhRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a579a3e0a4bdeafdb9aef7522c8c78b4dacfa1c6eb2ef9454764da836507a0f1","last_reissued_at":"2026-05-18T03:45:43.171396Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:45:43.171396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Han Liu, Lie Wang","submitted_at":"2012-09-11T20:53:45Z","abstract_excerpt":"We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts to choose the tuning parameter in finite sample settings. Computationally, our procedure is significantly faster than existing methods due to its tuning-insensitive property. Theoretically, the obtained estimator is simultaneously minimax optimal for precision matrix estimation under different norms. Empirically, we illustrate the advantages of our method using thorough simulated and real example"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.2437","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":"1209.2437","created_at":"2026-05-18T03:45:43.171498+00:00"},{"alias_kind":"arxiv_version","alias_value":"1209.2437v1","created_at":"2026-05-18T03:45:43.171498+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.2437","created_at":"2026-05-18T03:45:43.171498+00:00"},{"alias_kind":"pith_short_12","alias_value":"UV42HYFEXXVP","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_16","alias_value":"UV42HYFEXXVP3ONO","created_at":"2026-05-18T12:27:25.539911+00:00"},{"alias_kind":"pith_short_8","alias_value":"UV42HYFE","created_at":"2026-05-18T12:27:25.539911+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/UV42HYFEXXVP3ONO65JCZDDYWT","json":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT.json","graph_json":"https://pith.science/api/pith-number/UV42HYFEXXVP3ONO65JCZDDYWT/graph.json","events_json":"https://pith.science/api/pith-number/UV42HYFEXXVP3ONO65JCZDDYWT/events.json","paper":"https://pith.science/paper/UV42HYFE"},"agent_actions":{"view_html":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT","download_json":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT.json","view_paper":"https://pith.science/paper/UV42HYFE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1209.2437&json=true","fetch_graph":"https://pith.science/api/pith-number/UV42HYFEXXVP3ONO65JCZDDYWT/graph.json","fetch_events":"https://pith.science/api/pith-number/UV42HYFEXXVP3ONO65JCZDDYWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT/action/storage_attestation","attest_author":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT/action/author_attestation","sign_citation":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT/action/citation_signature","submit_replication":"https://pith.science/pith/UV42HYFEXXVP3ONO65JCZDDYWT/action/replication_record"}},"created_at":"2026-05-18T03:45:43.171498+00:00","updated_at":"2026-05-18T03:45:43.171498+00:00"}