{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:S33N245G3BZJGLHFZQGDUQZ46A","short_pith_number":"pith:S33N245G","schema_version":"1.0","canonical_sha256":"96f6dd73a6d872932ce5cc0c3a433cf03e7bd2ac4184679e6760a5f991820886","source":{"kind":"arxiv","id":"1902.00653","version":1},"attestation_state":"computed","paper":{"title":"On asymptotically efficient maximum likelihood estimation of linear functionals in Laplace measurement error models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Catia Scricciolo","submitted_at":"2019-02-02T06:55:59Z","abstract_excerpt":"Maximum likelihood estimation of linear functionals in the inverse problem of deconvolution is considered. Given observations of a random sample from a distribution $P_0\\equiv P_{F_0}$ indexed by a (potentially infinite-dimensional) parameter $F_0$, which is the distribution of the latent variable in a standard additive Laplace measurement error model, one wants to estimate a linear functional of $F_0$. Asymptotically efficient maximum likelihood estimation (MLE) of integral linear functionals of the mixing distribution $F_0$ in a convolution model with the Laplace kernel density is investigat"},"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":"1902.00653","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-02T06:55:59Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"a143438faf5d3b0dc98faad81dad0908174151c8267d26c8b71ec9cb82fa05ee","abstract_canon_sha256":"aea59401b1bd88fe1a242b87bd8c71206503af848fd8fff4f7fbd0b32ade2001"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:52.544273Z","signature_b64":"mREQJHuSOY3mSWYbgQqzV4URRCi/fFPQjdLz8HzsiA3/5cGOKTCm2uvJB3WoXZyNx3LTC1xEPGUJDqGdz7wmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96f6dd73a6d872932ce5cc0c3a433cf03e7bd2ac4184679e6760a5f991820886","last_reissued_at":"2026-05-17T23:54:52.543685Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:52.543685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On asymptotically efficient maximum likelihood estimation of linear functionals in Laplace measurement error models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Catia Scricciolo","submitted_at":"2019-02-02T06:55:59Z","abstract_excerpt":"Maximum likelihood estimation of linear functionals in the inverse problem of deconvolution is considered. Given observations of a random sample from a distribution $P_0\\equiv P_{F_0}$ indexed by a (potentially infinite-dimensional) parameter $F_0$, which is the distribution of the latent variable in a standard additive Laplace measurement error model, one wants to estimate a linear functional of $F_0$. Asymptotically efficient maximum likelihood estimation (MLE) of integral linear functionals of the mixing distribution $F_0$ in a convolution model with the Laplace kernel density is investigat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.00653","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":"1902.00653","created_at":"2026-05-17T23:54:52.543780+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.00653v1","created_at":"2026-05-17T23:54:52.543780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.00653","created_at":"2026-05-17T23:54:52.543780+00:00"},{"alias_kind":"pith_short_12","alias_value":"S33N245G3BZJ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"S33N245G3BZJGLHF","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"S33N245G","created_at":"2026-05-18T12:33:27.125529+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/S33N245G3BZJGLHFZQGDUQZ46A","json":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A.json","graph_json":"https://pith.science/api/pith-number/S33N245G3BZJGLHFZQGDUQZ46A/graph.json","events_json":"https://pith.science/api/pith-number/S33N245G3BZJGLHFZQGDUQZ46A/events.json","paper":"https://pith.science/paper/S33N245G"},"agent_actions":{"view_html":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A","download_json":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A.json","view_paper":"https://pith.science/paper/S33N245G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.00653&json=true","fetch_graph":"https://pith.science/api/pith-number/S33N245G3BZJGLHFZQGDUQZ46A/graph.json","fetch_events":"https://pith.science/api/pith-number/S33N245G3BZJGLHFZQGDUQZ46A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A/action/storage_attestation","attest_author":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A/action/author_attestation","sign_citation":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A/action/citation_signature","submit_replication":"https://pith.science/pith/S33N245G3BZJGLHFZQGDUQZ46A/action/replication_record"}},"created_at":"2026-05-17T23:54:52.543780+00:00","updated_at":"2026-05-17T23:54:52.543780+00:00"}