{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:73J4ZQJQPWC6GSUVRBPOI322KJ","short_pith_number":"pith:73J4ZQJQ","schema_version":"1.0","canonical_sha256":"fed3ccc1307d85e34a95885ee46f5a527516c3ca9a82d641a31492cf7d7ec417","source":{"kind":"arxiv","id":"1405.5240","version":2},"attestation_state":"computed","paper":{"title":"Statistical inference for spatial statistics defined in the Fourier domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Suhasini Subba Rao","submitted_at":"2014-05-20T20:38:39Z","abstract_excerpt":"A class of Fourier based statistics for irregular spaced spatial data is introduced, examples include, the Whittle likelihood, a parametric estimator of the covariance function based on the $L_{2}$-contrast function and a simple nonparametric estimator of the spatial autocovariance which is a non-negative function. The Fourier based statistic is a quadratic form of a discrete Fourier-type transform of the spatial data. Evaluation of the statistic is computationally tractable, requiring $O(nb^{})$ operations, where $b$ are the number Fourier frequencies used in the definition of the statistic a"},"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":"1405.5240","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-05-20T20:38:39Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"3219a10c595ef8761de971672cdc8414128b14f019345b5f7d704fe04a9c2a81","abstract_canon_sha256":"21ae5fb919967ff59ef25d18098ee8d78916f84db59bec701eff53dd79c5ddfc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:40.805522Z","signature_b64":"gihvxwE2rWao0y2Ch5mj2No9FcSEuImFbJmZuWttyJ6czJa8e+kiiAAcaaj67YHaAvzcoMiRbPSRP6m+BtmPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fed3ccc1307d85e34a95885ee46f5a527516c3ca9a82d641a31492cf7d7ec417","last_reissued_at":"2026-05-18T01:00:40.805053Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:40.805053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical inference for spatial statistics defined in the Fourier domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Suhasini Subba Rao","submitted_at":"2014-05-20T20:38:39Z","abstract_excerpt":"A class of Fourier based statistics for irregular spaced spatial data is introduced, examples include, the Whittle likelihood, a parametric estimator of the covariance function based on the $L_{2}$-contrast function and a simple nonparametric estimator of the spatial autocovariance which is a non-negative function. The Fourier based statistic is a quadratic form of a discrete Fourier-type transform of the spatial data. Evaluation of the statistic is computationally tractable, requiring $O(nb^{})$ operations, where $b$ are the number Fourier frequencies used in the definition of the statistic a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.5240","kind":"arxiv","version":2},"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":"1405.5240","created_at":"2026-05-18T01:00:40.805118+00:00"},{"alias_kind":"arxiv_version","alias_value":"1405.5240v2","created_at":"2026-05-18T01:00:40.805118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.5240","created_at":"2026-05-18T01:00:40.805118+00:00"},{"alias_kind":"pith_short_12","alias_value":"73J4ZQJQPWC6","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_16","alias_value":"73J4ZQJQPWC6GSUV","created_at":"2026-05-18T12:28:16.859392+00:00"},{"alias_kind":"pith_short_8","alias_value":"73J4ZQJQ","created_at":"2026-05-18T12:28:16.859392+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/73J4ZQJQPWC6GSUVRBPOI322KJ","json":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ.json","graph_json":"https://pith.science/api/pith-number/73J4ZQJQPWC6GSUVRBPOI322KJ/graph.json","events_json":"https://pith.science/api/pith-number/73J4ZQJQPWC6GSUVRBPOI322KJ/events.json","paper":"https://pith.science/paper/73J4ZQJQ"},"agent_actions":{"view_html":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ","download_json":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ.json","view_paper":"https://pith.science/paper/73J4ZQJQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1405.5240&json=true","fetch_graph":"https://pith.science/api/pith-number/73J4ZQJQPWC6GSUVRBPOI322KJ/graph.json","fetch_events":"https://pith.science/api/pith-number/73J4ZQJQPWC6GSUVRBPOI322KJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ/action/storage_attestation","attest_author":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ/action/author_attestation","sign_citation":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ/action/citation_signature","submit_replication":"https://pith.science/pith/73J4ZQJQPWC6GSUVRBPOI322KJ/action/replication_record"}},"created_at":"2026-05-18T01:00:40.805118+00:00","updated_at":"2026-05-18T01:00:40.805118+00:00"}