{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:R2SOM3DVI72MJATERP7TWC3KS6","short_pith_number":"pith:R2SOM3DV","schema_version":"1.0","canonical_sha256":"8ea4e66c7547f4c482648bff3b0b6a97a4853189130044540e3ac6827b8ee98b","source":{"kind":"arxiv","id":"1311.2276","version":1},"attestation_state":"computed","paper":{"title":"A Quantitative Evaluation Framework for Missing Value Imputation Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Johannes Gehrke, Rahul Kidambi, S. Sathiya Keerthi, Sundararajan Sellamanickam, Vijay Narayanan, Vinod Nair","submitted_at":"2013-11-10T14:17:47Z","abstract_excerpt":"We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill them in, there is currently no principled way to rank the algorithms using a quantitative metric. We develop a framework based on treating imputation evaluation as a problem of comparing two distributions and show how it can be used to compute quantitative metrics. We present an efficient procedure for applying this framework to practical datasets, demonstrate several metrics derived from the existing literature on c"},"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":"1311.2276","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-11-10T14:17:47Z","cross_cats_sorted":[],"title_canon_sha256":"e63eff859ad247d6eb0b9bda2b26d98344d858625c80c7ce445b3534171f9a2c","abstract_canon_sha256":"47cd0db5c5447526e8a9d257bdf281d1b76d5cf1eedf529b01922725c1ef6f56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:07:33.089759Z","signature_b64":"pxuK36kl4nGsl5TcRWY4loOuXADOTOrgCKPqvkI+aG/NtzFj50jyTbwC68FAjZvMWE5hoFXEKJ9uL8m8w7QhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ea4e66c7547f4c482648bff3b0b6a97a4853189130044540e3ac6827b8ee98b","last_reissued_at":"2026-05-18T03:07:33.089104Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:07:33.089104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Quantitative Evaluation Framework for Missing Value Imputation Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Johannes Gehrke, Rahul Kidambi, S. Sathiya Keerthi, Sundararajan Sellamanickam, Vijay Narayanan, Vinod Nair","submitted_at":"2013-11-10T14:17:47Z","abstract_excerpt":"We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill them in, there is currently no principled way to rank the algorithms using a quantitative metric. We develop a framework based on treating imputation evaluation as a problem of comparing two distributions and show how it can be used to compute quantitative metrics. We present an efficient procedure for applying this framework to practical datasets, demonstrate several metrics derived from the existing literature on c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.2276","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":"1311.2276","created_at":"2026-05-18T03:07:33.089190+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.2276v1","created_at":"2026-05-18T03:07:33.089190+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.2276","created_at":"2026-05-18T03:07:33.089190+00:00"},{"alias_kind":"pith_short_12","alias_value":"R2SOM3DVI72M","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_16","alias_value":"R2SOM3DVI72MJATE","created_at":"2026-05-18T12:27:57.521954+00:00"},{"alias_kind":"pith_short_8","alias_value":"R2SOM3DV","created_at":"2026-05-18T12:27:57.521954+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/R2SOM3DVI72MJATERP7TWC3KS6","json":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6.json","graph_json":"https://pith.science/api/pith-number/R2SOM3DVI72MJATERP7TWC3KS6/graph.json","events_json":"https://pith.science/api/pith-number/R2SOM3DVI72MJATERP7TWC3KS6/events.json","paper":"https://pith.science/paper/R2SOM3DV"},"agent_actions":{"view_html":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6","download_json":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6.json","view_paper":"https://pith.science/paper/R2SOM3DV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.2276&json=true","fetch_graph":"https://pith.science/api/pith-number/R2SOM3DVI72MJATERP7TWC3KS6/graph.json","fetch_events":"https://pith.science/api/pith-number/R2SOM3DVI72MJATERP7TWC3KS6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6/action/storage_attestation","attest_author":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6/action/author_attestation","sign_citation":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6/action/citation_signature","submit_replication":"https://pith.science/pith/R2SOM3DVI72MJATERP7TWC3KS6/action/replication_record"}},"created_at":"2026-05-18T03:07:33.089190+00:00","updated_at":"2026-05-18T03:07:33.089190+00:00"}