{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:DMFZAVZT7SLUIXQHWWYJB6VSYU","short_pith_number":"pith:DMFZAVZT","schema_version":"1.0","canonical_sha256":"1b0b905733fc97445e07b5b090fab2c50aa4ebcb8d22fd5e0303e85e8bfd71b5","source":{"kind":"arxiv","id":"1312.5839","version":1},"attestation_state":"computed","paper":{"title":"Model selection in a sparse heterogeneous framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Laurent Cavalier, Markus Rei{\\ss}","submitted_at":"2013-12-20T08:05:01Z","abstract_excerpt":"We consider a Gaussian sequence space model $X_{\\lambda}=f_{\\lambda} + \\xi_{\\lambda},$ where $\\xi $ has a diagonal covariance matrix $\\Sigma=\\diag(\\sigma_\\lambda ^2)$. We consider the situation where the parameter vector $(f_{\\lambda})$ is sparse. Our goal is to estimate the unknown parameter by a model selection approach. The heterogenous case is much more involved than the direct model. Indeed, there is no more symmetry inside the stochastic process that one needs to control since each empirical coefficient has its own variance. The problem and the penalty do not only depend on the number of"},"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":"1312.5839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-12-20T08:05:01Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e760608e2661479b8435bc2c286706f2a5db092134900c5ed59c22532157d30a","abstract_canon_sha256":"d764528d9439c432da5d2e80e23516c20011a4ee7550760a1b250bf88f9b7481"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:04:07.712016Z","signature_b64":"i9ohkryAUFTR/rfxb5FMVwjX6x/9FvGiXZJkhLFdOLhzVAD9RX242nagF+UHukW6y8jI2VOcXgjIa6C3By4qCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b0b905733fc97445e07b5b090fab2c50aa4ebcb8d22fd5e0303e85e8bfd71b5","last_reissued_at":"2026-05-18T03:04:07.711520Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:04:07.711520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model selection in a sparse heterogeneous framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Laurent Cavalier, Markus Rei{\\ss}","submitted_at":"2013-12-20T08:05:01Z","abstract_excerpt":"We consider a Gaussian sequence space model $X_{\\lambda}=f_{\\lambda} + \\xi_{\\lambda},$ where $\\xi $ has a diagonal covariance matrix $\\Sigma=\\diag(\\sigma_\\lambda ^2)$. We consider the situation where the parameter vector $(f_{\\lambda})$ is sparse. Our goal is to estimate the unknown parameter by a model selection approach. The heterogenous case is much more involved than the direct model. Indeed, there is no more symmetry inside the stochastic process that one needs to control since each empirical coefficient has its own variance. The problem and the penalty do not only depend on the number of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.5839","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":"1312.5839","created_at":"2026-05-18T03:04:07.711596+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.5839v1","created_at":"2026-05-18T03:04:07.711596+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.5839","created_at":"2026-05-18T03:04:07.711596+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMFZAVZT7SLU","created_at":"2026-05-18T12:27:43.054852+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMFZAVZT7SLUIXQH","created_at":"2026-05-18T12:27:43.054852+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMFZAVZT","created_at":"2026-05-18T12:27:43.054852+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/DMFZAVZT7SLUIXQHWWYJB6VSYU","json":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU.json","graph_json":"https://pith.science/api/pith-number/DMFZAVZT7SLUIXQHWWYJB6VSYU/graph.json","events_json":"https://pith.science/api/pith-number/DMFZAVZT7SLUIXQHWWYJB6VSYU/events.json","paper":"https://pith.science/paper/DMFZAVZT"},"agent_actions":{"view_html":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU","download_json":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU.json","view_paper":"https://pith.science/paper/DMFZAVZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.5839&json=true","fetch_graph":"https://pith.science/api/pith-number/DMFZAVZT7SLUIXQHWWYJB6VSYU/graph.json","fetch_events":"https://pith.science/api/pith-number/DMFZAVZT7SLUIXQHWWYJB6VSYU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU/action/storage_attestation","attest_author":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU/action/author_attestation","sign_citation":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU/action/citation_signature","submit_replication":"https://pith.science/pith/DMFZAVZT7SLUIXQHWWYJB6VSYU/action/replication_record"}},"created_at":"2026-05-18T03:04:07.711596+00:00","updated_at":"2026-05-18T03:04:07.711596+00:00"}