{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:LY3TEH5D336PJLJX4FP6T7SW45","short_pith_number":"pith:LY3TEH5D","schema_version":"1.0","canonical_sha256":"5e37321fa3defcf4ad37e15fe9fe56e74665a6961ee29ab62efd3d7e45d5a84f","source":{"kind":"arxiv","id":"1508.01681","version":1},"attestation_state":"computed","paper":{"title":"Joint estimation and model order selection for one dimensional ARMA models via convex optimization: a nuclear norm penalization approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.TH"],"primary_cat":"math.ST","authors_text":"Basad Ali Hussain Al-sarray, St\\'ephane Chr\\'etien, Tianwen Wei","submitted_at":"2015-08-07T13:21:15Z","abstract_excerpt":"The problem of estimating ARMA models is computationally interesting due to the nonconcavity of the log-likelihood function. Recent results were based on the convex minimization. Joint model selection using penalization by a convex norm, e.g. the nuclear norm of a certain matrix related to the state space formulation was extensively studied from a computational viewpoint. The goal of the present short note is to present a theoretical study of a nuclear norm penalization based variant of the method of \\cite{Bauer:Automatica05,Bauer:EconTh05} under the assumption of a Gaussian noise process."},"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":"1508.01681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-08-07T13:21:15Z","cross_cats_sorted":["stat.CO","stat.TH"],"title_canon_sha256":"6b00f2a11e8ee4d4a49bd5f19e426ef07114a2739162a2e9913aff7f2006a04c","abstract_canon_sha256":"7837a3d3aaa216ca2501ae2a1ac45374c039347d21490ca7cd47d07a373d7b90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:35:39.032180Z","signature_b64":"e1KJJ3fxaybSdoay8fJJB5lc5YM6e7kpQvv9ZiYK3F2OOBZ80O/raU43L6a9Ng+93hZWUygu4kOXOlllGrCVAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e37321fa3defcf4ad37e15fe9fe56e74665a6961ee29ab62efd3d7e45d5a84f","last_reissued_at":"2026-05-18T01:35:39.031560Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:35:39.031560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint estimation and model order selection for one dimensional ARMA models via convex optimization: a nuclear norm penalization approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.TH"],"primary_cat":"math.ST","authors_text":"Basad Ali Hussain Al-sarray, St\\'ephane Chr\\'etien, Tianwen Wei","submitted_at":"2015-08-07T13:21:15Z","abstract_excerpt":"The problem of estimating ARMA models is computationally interesting due to the nonconcavity of the log-likelihood function. Recent results were based on the convex minimization. Joint model selection using penalization by a convex norm, e.g. the nuclear norm of a certain matrix related to the state space formulation was extensively studied from a computational viewpoint. The goal of the present short note is to present a theoretical study of a nuclear norm penalization based variant of the method of \\cite{Bauer:Automatica05,Bauer:EconTh05} under the assumption of a Gaussian noise process."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.01681","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":"1508.01681","created_at":"2026-05-18T01:35:39.031672+00:00"},{"alias_kind":"arxiv_version","alias_value":"1508.01681v1","created_at":"2026-05-18T01:35:39.031672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.01681","created_at":"2026-05-18T01:35:39.031672+00:00"},{"alias_kind":"pith_short_12","alias_value":"LY3TEH5D336P","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"LY3TEH5D336PJLJX","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"LY3TEH5D","created_at":"2026-05-18T12:29:29.992203+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/LY3TEH5D336PJLJX4FP6T7SW45","json":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45.json","graph_json":"https://pith.science/api/pith-number/LY3TEH5D336PJLJX4FP6T7SW45/graph.json","events_json":"https://pith.science/api/pith-number/LY3TEH5D336PJLJX4FP6T7SW45/events.json","paper":"https://pith.science/paper/LY3TEH5D"},"agent_actions":{"view_html":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45","download_json":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45.json","view_paper":"https://pith.science/paper/LY3TEH5D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1508.01681&json=true","fetch_graph":"https://pith.science/api/pith-number/LY3TEH5D336PJLJX4FP6T7SW45/graph.json","fetch_events":"https://pith.science/api/pith-number/LY3TEH5D336PJLJX4FP6T7SW45/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45/action/storage_attestation","attest_author":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45/action/author_attestation","sign_citation":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45/action/citation_signature","submit_replication":"https://pith.science/pith/LY3TEH5D336PJLJX4FP6T7SW45/action/replication_record"}},"created_at":"2026-05-18T01:35:39.031672+00:00","updated_at":"2026-05-18T01:35:39.031672+00:00"}