{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:YATYSVLFYZAVKEUMGGCXR5LOJA","short_pith_number":"pith:YATYSVLF","schema_version":"1.0","canonical_sha256":"c027895565c64155128c318578f56e480ac4d33f73172c64af1b632131c9941d","source":{"kind":"arxiv","id":"1405.2601","version":1},"attestation_state":"computed","paper":{"title":"LP Approach to Statistical Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Emanuel Parzen, Subhadeep Mukhopadhyay","submitted_at":"2014-05-11T23:16:37Z","abstract_excerpt":"We present an approach to statistical data modeling and exploratory data analysis called `LP Statistical Data Science.' It aims to generalize and unify traditional and novel statistical measures, methods, and exploratory tools. This article outlines fundamental concepts along with real-data examples to illustrate how the `LP Statistical Algorithm' can systematically tackle different varieties of data types, data patterns, and data structures under a coherent theoretical framework. A fundamental role is played by specially designed orthonormal basis of a random variable X for linear (Hilbert sp"},"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":"1405.2601","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-05-11T23:16:37Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"09304b10adab194f40a21d9601bfa462a65febe31573191b3d222a4f1f9d7795","abstract_canon_sha256":"df74663939ae19100925936e807315c9872cef6490fa5f1a94d486a8b760e021"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:05.154145Z","signature_b64":"TE9IrIKy7/t8lxyR1eqbbFNKwYBLw6e0dmxJmn1qi7k4UW+E+2wVuQXz2V20b/+nvghaPTGN4cSlfjWCOtPBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c027895565c64155128c318578f56e480ac4d33f73172c64af1b632131c9941d","last_reissued_at":"2026-05-18T02:52:05.153600Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:05.153600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LP Approach to Statistical Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Emanuel Parzen, Subhadeep Mukhopadhyay","submitted_at":"2014-05-11T23:16:37Z","abstract_excerpt":"We present an approach to statistical data modeling and exploratory data analysis called `LP Statistical Data Science.' It aims to generalize and unify traditional and novel statistical measures, methods, and exploratory tools. This article outlines fundamental concepts along with real-data examples to illustrate how the `LP Statistical Algorithm' can systematically tackle different varieties of data types, data patterns, and data structures under a coherent theoretical framework. A fundamental role is played by specially designed orthonormal basis of a random variable X for linear (Hilbert sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.2601","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":"1405.2601","created_at":"2026-05-18T02:52:05.153698+00:00"},{"alias_kind":"arxiv_version","alias_value":"1405.2601v1","created_at":"2026-05-18T02:52:05.153698+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.2601","created_at":"2026-05-18T02:52:05.153698+00:00"},{"alias_kind":"pith_short_12","alias_value":"YATYSVLFYZAV","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_16","alias_value":"YATYSVLFYZAVKEUM","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_8","alias_value":"YATYSVLF","created_at":"2026-05-18T12:28:57.508820+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/YATYSVLFYZAVKEUMGGCXR5LOJA","json":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA.json","graph_json":"https://pith.science/api/pith-number/YATYSVLFYZAVKEUMGGCXR5LOJA/graph.json","events_json":"https://pith.science/api/pith-number/YATYSVLFYZAVKEUMGGCXR5LOJA/events.json","paper":"https://pith.science/paper/YATYSVLF"},"agent_actions":{"view_html":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA","download_json":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA.json","view_paper":"https://pith.science/paper/YATYSVLF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1405.2601&json=true","fetch_graph":"https://pith.science/api/pith-number/YATYSVLFYZAVKEUMGGCXR5LOJA/graph.json","fetch_events":"https://pith.science/api/pith-number/YATYSVLFYZAVKEUMGGCXR5LOJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA/action/storage_attestation","attest_author":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA/action/author_attestation","sign_citation":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA/action/citation_signature","submit_replication":"https://pith.science/pith/YATYSVLFYZAVKEUMGGCXR5LOJA/action/replication_record"}},"created_at":"2026-05-18T02:52:05.153698+00:00","updated_at":"2026-05-18T02:52:05.153698+00:00"}