{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:IFKAJL4BSGSPSXPL3LKM7CGF55","short_pith_number":"pith:IFKAJL4B","schema_version":"1.0","canonical_sha256":"415404af8191a4f95debdad4cf88c5ef4d2e12a35bd716c74ba3042ea38742ad","source":{"kind":"arxiv","id":"1201.0846","version":1},"attestation_state":"computed","paper":{"title":"Using complex surveys to estimate the $L_1$-median of a functional variable: application to electricity load curves","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.OT","authors_text":"Camelia Goga, Mohamed Chaouch","submitted_at":"2012-01-04T08:53:42Z","abstract_excerpt":"Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in \\'Electricit\\'e De France (EDF), class load profiles are estimated using point-wise mean function. Unfortunately, it is well known that the mean is highly sensitive to the presence of outliers, such as one or more consumers with unusually high-levels of consumption. In this paper, we propose an alternative to the mean profile: the $L_1$-median profile which is more robust. When dealing with large datasets of functional data (load curves for example), survey sampling approaches are usef"},"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":"1201.0846","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.OT","submitted_at":"2012-01-04T08:53:42Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"a2f41fca3eca80e9c37cae0d7607659fa34f956a3a4b18d18d0c464ff9f0e6a6","abstract_canon_sha256":"6d6590a1b9b58c91880cc546f1cee8192b920da582d7986b8dd9a1bcd5dde1cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:45:00.781418Z","signature_b64":"B4KglwcQmryIv7Es+e1CkJ//1FH9+1w0UyvxIQqLlSATjRp5oKkh4BP59qpXs4C/bYqjUCMGCc/WIE8f9pbGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"415404af8191a4f95debdad4cf88c5ef4d2e12a35bd716c74ba3042ea38742ad","last_reissued_at":"2026-05-18T03:45:00.780690Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:45:00.780690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using complex surveys to estimate the $L_1$-median of a functional variable: application to electricity load curves","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.OT","authors_text":"Camelia Goga, Mohamed Chaouch","submitted_at":"2012-01-04T08:53:42Z","abstract_excerpt":"Mean profiles are widely used as indicators of the electricity consumption habits of customers. Currently, in \\'Electricit\\'e De France (EDF), class load profiles are estimated using point-wise mean function. Unfortunately, it is well known that the mean is highly sensitive to the presence of outliers, such as one or more consumers with unusually high-levels of consumption. In this paper, we propose an alternative to the mean profile: the $L_1$-median profile which is more robust. When dealing with large datasets of functional data (load curves for example), survey sampling approaches are usef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.0846","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":"1201.0846","created_at":"2026-05-18T03:45:00.780801+00:00"},{"alias_kind":"arxiv_version","alias_value":"1201.0846v1","created_at":"2026-05-18T03:45:00.780801+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.0846","created_at":"2026-05-18T03:45:00.780801+00:00"},{"alias_kind":"pith_short_12","alias_value":"IFKAJL4BSGSP","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_16","alias_value":"IFKAJL4BSGSPSXPL","created_at":"2026-05-18T12:27:09.501522+00:00"},{"alias_kind":"pith_short_8","alias_value":"IFKAJL4B","created_at":"2026-05-18T12:27:09.501522+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/IFKAJL4BSGSPSXPL3LKM7CGF55","json":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55.json","graph_json":"https://pith.science/api/pith-number/IFKAJL4BSGSPSXPL3LKM7CGF55/graph.json","events_json":"https://pith.science/api/pith-number/IFKAJL4BSGSPSXPL3LKM7CGF55/events.json","paper":"https://pith.science/paper/IFKAJL4B"},"agent_actions":{"view_html":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55","download_json":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55.json","view_paper":"https://pith.science/paper/IFKAJL4B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1201.0846&json=true","fetch_graph":"https://pith.science/api/pith-number/IFKAJL4BSGSPSXPL3LKM7CGF55/graph.json","fetch_events":"https://pith.science/api/pith-number/IFKAJL4BSGSPSXPL3LKM7CGF55/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55/action/storage_attestation","attest_author":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55/action/author_attestation","sign_citation":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55/action/citation_signature","submit_replication":"https://pith.science/pith/IFKAJL4BSGSPSXPL3LKM7CGF55/action/replication_record"}},"created_at":"2026-05-18T03:45:00.780801+00:00","updated_at":"2026-05-18T03:45:00.780801+00:00"}