{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:OUQV7OWEBT2LXHVETGEGPUW6TG","short_pith_number":"pith:OUQV7OWE","schema_version":"1.0","canonical_sha256":"75215fbac40cf4bb9ea4998867d2de99bbb409ece128d5ae2636bb465e0b129a","source":{"kind":"arxiv","id":"1109.0320","version":2},"attestation_state":"computed","paper":{"title":"Penalized maximum likelihood estimation and variable selection in geostatistics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Haonan Wang, Jun Zhu, Tingjin Chu","submitted_at":"2011-09-01T22:45:31Z","abstract_excerpt":"We consider the problem of selecting covariates in spatial linear models with Gaussian process errors. Penalized maximum likelihood estimation (PMLE) that enables simultaneous variable selection and parameter estimation is developed and, for ease of computation, PMLE is approximated by one-step sparse estimation (OSE). To further improve computational efficiency, particularly with large sample sizes, we propose penalized maximum covariance-tapered likelihood estimation (PMLE$_{\\mathrm{T}}$) and its one-step sparse estimation (OSE$_{\\mathrm{T}}$). General forms of penalty functions with an emph"},"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":"1109.0320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-09-01T22:45:31Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"a817655bab8cdfad7d81b6e2a9679abcdac37c1e050f43ab24fddee697205fc6","abstract_canon_sha256":"b4c892bc9fd9a6eecfe3a6f0bce11695bf5dbd00ae462cae1e1c90192102a01b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:01:39.481846Z","signature_b64":"k0kqBjvChP333vw1nXcMZ33wWMnwQewy0zQpH/5gaL3aIMhws+5uL6RXze15w1Zm1wp8U5FWrslIgxXTt4sCBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75215fbac40cf4bb9ea4998867d2de99bbb409ece128d5ae2636bb465e0b129a","last_reissued_at":"2026-05-18T04:01:39.481272Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:01:39.481272Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Penalized maximum likelihood estimation and variable selection in geostatistics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Haonan Wang, Jun Zhu, Tingjin Chu","submitted_at":"2011-09-01T22:45:31Z","abstract_excerpt":"We consider the problem of selecting covariates in spatial linear models with Gaussian process errors. Penalized maximum likelihood estimation (PMLE) that enables simultaneous variable selection and parameter estimation is developed and, for ease of computation, PMLE is approximated by one-step sparse estimation (OSE). To further improve computational efficiency, particularly with large sample sizes, we propose penalized maximum covariance-tapered likelihood estimation (PMLE$_{\\mathrm{T}}$) and its one-step sparse estimation (OSE$_{\\mathrm{T}}$). General forms of penalty functions with an emph"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1109.0320","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1109.0320","created_at":"2026-05-18T04:01:39.481355+00:00"},{"alias_kind":"arxiv_version","alias_value":"1109.0320v2","created_at":"2026-05-18T04:01:39.481355+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1109.0320","created_at":"2026-05-18T04:01:39.481355+00:00"},{"alias_kind":"pith_short_12","alias_value":"OUQV7OWEBT2L","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_16","alias_value":"OUQV7OWEBT2LXHVE","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_8","alias_value":"OUQV7OWE","created_at":"2026-05-18T12:26:37.096874+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/OUQV7OWEBT2LXHVETGEGPUW6TG","json":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG.json","graph_json":"https://pith.science/api/pith-number/OUQV7OWEBT2LXHVETGEGPUW6TG/graph.json","events_json":"https://pith.science/api/pith-number/OUQV7OWEBT2LXHVETGEGPUW6TG/events.json","paper":"https://pith.science/paper/OUQV7OWE"},"agent_actions":{"view_html":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG","download_json":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG.json","view_paper":"https://pith.science/paper/OUQV7OWE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1109.0320&json=true","fetch_graph":"https://pith.science/api/pith-number/OUQV7OWEBT2LXHVETGEGPUW6TG/graph.json","fetch_events":"https://pith.science/api/pith-number/OUQV7OWEBT2LXHVETGEGPUW6TG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG/action/storage_attestation","attest_author":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG/action/author_attestation","sign_citation":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG/action/citation_signature","submit_replication":"https://pith.science/pith/OUQV7OWEBT2LXHVETGEGPUW6TG/action/replication_record"}},"created_at":"2026-05-18T04:01:39.481355+00:00","updated_at":"2026-05-18T04:01:39.481355+00:00"}