{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MEIJCI5KMOVXVJKK32OBK2DEYK","short_pith_number":"pith:MEIJCI5K","schema_version":"1.0","canonical_sha256":"61109123aa63ab7aa54ade9c156864c288b87d390e96f6606097bb93c2d645dd","source":{"kind":"arxiv","id":"1601.06000","version":1},"attestation_state":"computed","paper":{"title":"Partially linear additive quantile regression in ultra-high dimension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ben Sherwood, Lan Wang","submitted_at":"2016-01-22T13:46:56Z","abstract_excerpt":"We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. (2) The sparsity level is allowed to be different at different quantile levels. (3) The partially linear additive structure accommodates nonlinearity and circumvents the curse of dimensionality. (4) It is naturally robust to heavy-tailed distributions. In this p"},"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":"1601.06000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-01-22T13:46:56Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"bb0492eb4b013019d5890280cd224604ec9b0c65f698f609e7178783873a83c9","abstract_canon_sha256":"df0670f543de7216a83baf01123e63f871997fbd24a6095fe8ad8336c4555d6a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:09.617389Z","signature_b64":"gZw7Xej12AUN12qwvKOFSoryE2z/HimrTXr+DUitAy+udQ5sosk9K6LnjGpgUc4Fi7TWy5VXZXNFqeZyqn4aBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61109123aa63ab7aa54ade9c156864c288b87d390e96f6606097bb93c2d645dd","last_reissued_at":"2026-05-18T01:22:09.616909Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:09.616909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Partially linear additive quantile regression in ultra-high dimension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ben Sherwood, Lan Wang","submitted_at":"2016-01-22T13:46:56Z","abstract_excerpt":"We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. (2) The sparsity level is allowed to be different at different quantile levels. (3) The partially linear additive structure accommodates nonlinearity and circumvents the curse of dimensionality. (4) It is naturally robust to heavy-tailed distributions. In this p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.06000","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":"1601.06000","created_at":"2026-05-18T01:22:09.616983+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.06000v1","created_at":"2026-05-18T01:22:09.616983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.06000","created_at":"2026-05-18T01:22:09.616983+00:00"},{"alias_kind":"pith_short_12","alias_value":"MEIJCI5KMOVX","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MEIJCI5KMOVXVJKK","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MEIJCI5K","created_at":"2026-05-18T12:30:32.724797+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/MEIJCI5KMOVXVJKK32OBK2DEYK","json":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK.json","graph_json":"https://pith.science/api/pith-number/MEIJCI5KMOVXVJKK32OBK2DEYK/graph.json","events_json":"https://pith.science/api/pith-number/MEIJCI5KMOVXVJKK32OBK2DEYK/events.json","paper":"https://pith.science/paper/MEIJCI5K"},"agent_actions":{"view_html":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK","download_json":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK.json","view_paper":"https://pith.science/paper/MEIJCI5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.06000&json=true","fetch_graph":"https://pith.science/api/pith-number/MEIJCI5KMOVXVJKK32OBK2DEYK/graph.json","fetch_events":"https://pith.science/api/pith-number/MEIJCI5KMOVXVJKK32OBK2DEYK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK/action/storage_attestation","attest_author":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK/action/author_attestation","sign_citation":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK/action/citation_signature","submit_replication":"https://pith.science/pith/MEIJCI5KMOVXVJKK32OBK2DEYK/action/replication_record"}},"created_at":"2026-05-18T01:22:09.616983+00:00","updated_at":"2026-05-18T01:22:09.616983+00:00"}