{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:Q5SZD4WXIAZXK6LJO6OZYD2CMN","short_pith_number":"pith:Q5SZD4WX","schema_version":"1.0","canonical_sha256":"876591f2d74033757969779d9c0f426364d49735531f158721f733334707661d","source":{"kind":"arxiv","id":"1603.08815","version":1},"attestation_state":"computed","paper":{"title":"Spectral M-estimation with Applications to Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.CO","authors_text":"Dustin Tran, Finale Doshi-Velez, Minjae Kim","submitted_at":"2016-03-29T15:34:29Z","abstract_excerpt":"Method of moment estimators exhibit appealing statistical properties, such as asymptotic unbiasedness, for nonconvex problems. However, they typically require a large number of samples and are extremely sensitive to model misspecification. In this paper, we apply the framework of M-estimation to develop both a generalized method of moments procedure and a principled method for regularization. Our proposed M-estimator obtains optimal sample efficiency rates (in the class of moment-based estimators) and the same well-known rates on prediction accuracy as other spectral estimators. It also makes "},"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":"1603.08815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-03-29T15:34:29Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"92d9f27d519231e6ce09d037390f5eb6e920942302d80ecc798024c22a2203c4","abstract_canon_sha256":"7120e14ce6594834456b25aff82d7eb39de62b3b0b5374789bfe3071c7403b1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:05.258468Z","signature_b64":"6RAqtLdYhUcTbhnozLjCata8DUzdbvSaBmz/QHayWA6nI0lhVIhMw4wDLkplp0ZsR7xOeuHVzvcPmJrdf4XMAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"876591f2d74033757969779d9c0f426364d49735531f158721f733334707661d","last_reissued_at":"2026-05-18T01:18:05.257829Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:05.257829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral M-estimation with Applications to Hidden Markov Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.CO","authors_text":"Dustin Tran, Finale Doshi-Velez, Minjae Kim","submitted_at":"2016-03-29T15:34:29Z","abstract_excerpt":"Method of moment estimators exhibit appealing statistical properties, such as asymptotic unbiasedness, for nonconvex problems. However, they typically require a large number of samples and are extremely sensitive to model misspecification. In this paper, we apply the framework of M-estimation to develop both a generalized method of moments procedure and a principled method for regularization. Our proposed M-estimator obtains optimal sample efficiency rates (in the class of moment-based estimators) and the same well-known rates on prediction accuracy as other spectral estimators. It also makes "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08815","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":"1603.08815","created_at":"2026-05-18T01:18:05.257956+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.08815v1","created_at":"2026-05-18T01:18:05.257956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08815","created_at":"2026-05-18T01:18:05.257956+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q5SZD4WXIAZX","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q5SZD4WXIAZXK6LJ","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q5SZD4WX","created_at":"2026-05-18T12:30:39.010887+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/Q5SZD4WXIAZXK6LJO6OZYD2CMN","json":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN.json","graph_json":"https://pith.science/api/pith-number/Q5SZD4WXIAZXK6LJO6OZYD2CMN/graph.json","events_json":"https://pith.science/api/pith-number/Q5SZD4WXIAZXK6LJO6OZYD2CMN/events.json","paper":"https://pith.science/paper/Q5SZD4WX"},"agent_actions":{"view_html":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN","download_json":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN.json","view_paper":"https://pith.science/paper/Q5SZD4WX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.08815&json=true","fetch_graph":"https://pith.science/api/pith-number/Q5SZD4WXIAZXK6LJO6OZYD2CMN/graph.json","fetch_events":"https://pith.science/api/pith-number/Q5SZD4WXIAZXK6LJO6OZYD2CMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN/action/storage_attestation","attest_author":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN/action/author_attestation","sign_citation":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN/action/citation_signature","submit_replication":"https://pith.science/pith/Q5SZD4WXIAZXK6LJO6OZYD2CMN/action/replication_record"}},"created_at":"2026-05-18T01:18:05.257956+00:00","updated_at":"2026-05-18T01:18:05.257956+00:00"}