{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:S26J5NCO3ESE3WW6SR5HON7RXI","short_pith_number":"pith:S26J5NCO","canonical_record":{"source":{"id":"1610.08360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-26T14:49:30Z","cross_cats_sorted":[],"title_canon_sha256":"edb4a61a6dce43334ff4134453aa16f504593a7afe96489cbc7f1a67501edb0d","abstract_canon_sha256":"51b514c0157a93c941895d0828c2857ff4eecbd306a1cf022e9f8cf7c650fcc6"},"schema_version":"1.0"},"canonical_sha256":"96bc9eb44ed9244ddade947a7737f1ba0da7100e3b5e08937d92f7161d18b342","source":{"kind":"arxiv","id":"1610.08360","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08360","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08360v1","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08360","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"pith_short_12","alias_value":"S26J5NCO3ESE","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"S26J5NCO3ESE3WW6","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"S26J5NCO","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:S26J5NCO3ESE3WW6SR5HON7RXI","target":"record","payload":{"canonical_record":{"source":{"id":"1610.08360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-26T14:49:30Z","cross_cats_sorted":[],"title_canon_sha256":"edb4a61a6dce43334ff4134453aa16f504593a7afe96489cbc7f1a67501edb0d","abstract_canon_sha256":"51b514c0157a93c941895d0828c2857ff4eecbd306a1cf022e9f8cf7c650fcc6"},"schema_version":"1.0"},"canonical_sha256":"96bc9eb44ed9244ddade947a7737f1ba0da7100e3b5e08937d92f7161d18b342","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:09.861779Z","signature_b64":"VvuU4E+oUtA4TruRA71M+xHoJO+d3OgwqHffgUhSkaTUXWuQ+CqI+Cmr8nNEiHTs0XZE+IGku2emQnYThOW8Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96bc9eb44ed9244ddade947a7737f1ba0da7100e3b5e08937d92f7161d18b342","last_reissued_at":"2026-05-18T01:01:09.861112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:09.861112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.08360","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:01:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S4s/FzaahRWqDD1whkZCTdx1khM+BUS8bcNE526Bhk29q0ZNlHEE+F6iF3Zro+N9u0dmxb6DUEU+7VzT3LLdDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:41:51.666462Z"},"content_sha256":"1bd006f09222c92a508c044718f0269ad1b9cfab275d75727e7a70472e0e3346","schema_version":"1.0","event_id":"sha256:1bd006f09222c92a508c044718f0269ad1b9cfab275d75727e7a70472e0e3346"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:S26J5NCO3ESE3WW6SR5HON7RXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficiently estimating the error distribution in nonparametric regression with responses missing at random","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Justin Chown, Ursula U. M\\\"uller","submitted_at":"2016-10-26T14:49:30Z","abstract_excerpt":"This article considers nonparametric regression models with multivariate covariates and with responses missing at random. We estimate the regression function with a local polynomial smoother. The residual-based empirical distribution function that only uses complete cases, i.e. residuals that can actually be constructed from the data, is shown to be efficient in the sense of H\\'ajek and Le Cam. In the proofs we derive, more generally, the efficient influence function for estimating an arbitrary linear functional of the error distribution; this covers the distribution function as a special case"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08360","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:01:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qkR3oiIhC2jKmQoxxto347IJIg4o0zggu4gA/u+ICzGDIgjOGErLfMuB/5t2/sDc57Weg+SYrQCDzISdZj1vAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T01:41:51.666811Z"},"content_sha256":"45af6236ce058fbafcd797d5deae3c4205bc86cc93442b148c4548761e174679","schema_version":"1.0","event_id":"sha256:45af6236ce058fbafcd797d5deae3c4205bc86cc93442b148c4548761e174679"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S26J5NCO3ESE3WW6SR5HON7RXI/bundle.json","state_url":"https://pith.science/pith/S26J5NCO3ESE3WW6SR5HON7RXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S26J5NCO3ESE3WW6SR5HON7RXI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T01:41:51Z","links":{"resolver":"https://pith.science/pith/S26J5NCO3ESE3WW6SR5HON7RXI","bundle":"https://pith.science/pith/S26J5NCO3ESE3WW6SR5HON7RXI/bundle.json","state":"https://pith.science/pith/S26J5NCO3ESE3WW6SR5HON7RXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S26J5NCO3ESE3WW6SR5HON7RXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:S26J5NCO3ESE3WW6SR5HON7RXI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"51b514c0157a93c941895d0828c2857ff4eecbd306a1cf022e9f8cf7c650fcc6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-26T14:49:30Z","title_canon_sha256":"edb4a61a6dce43334ff4134453aa16f504593a7afe96489cbc7f1a67501edb0d"},"schema_version":"1.0","source":{"id":"1610.08360","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08360","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08360v1","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08360","created_at":"2026-05-18T01:01:09Z"},{"alias_kind":"pith_short_12","alias_value":"S26J5NCO3ESE","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"S26J5NCO3ESE3WW6","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"S26J5NCO","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:45af6236ce058fbafcd797d5deae3c4205bc86cc93442b148c4548761e174679","target":"graph","created_at":"2026-05-18T01:01:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This article considers nonparametric regression models with multivariate covariates and with responses missing at random. We estimate the regression function with a local polynomial smoother. The residual-based empirical distribution function that only uses complete cases, i.e. residuals that can actually be constructed from the data, is shown to be efficient in the sense of H\\'ajek and Le Cam. In the proofs we derive, more generally, the efficient influence function for estimating an arbitrary linear functional of the error distribution; this covers the distribution function as a special case","authors_text":"Justin Chown, Ursula U. M\\\"uller","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-26T14:49:30Z","title":"Efficiently estimating the error distribution in nonparametric regression with responses missing at random"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08360","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1bd006f09222c92a508c044718f0269ad1b9cfab275d75727e7a70472e0e3346","target":"record","created_at":"2026-05-18T01:01:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"51b514c0157a93c941895d0828c2857ff4eecbd306a1cf022e9f8cf7c650fcc6","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-26T14:49:30Z","title_canon_sha256":"edb4a61a6dce43334ff4134453aa16f504593a7afe96489cbc7f1a67501edb0d"},"schema_version":"1.0","source":{"id":"1610.08360","kind":"arxiv","version":1}},"canonical_sha256":"96bc9eb44ed9244ddade947a7737f1ba0da7100e3b5e08937d92f7161d18b342","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"96bc9eb44ed9244ddade947a7737f1ba0da7100e3b5e08937d92f7161d18b342","first_computed_at":"2026-05-18T01:01:09.861112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:01:09.861112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VvuU4E+oUtA4TruRA71M+xHoJO+d3OgwqHffgUhSkaTUXWuQ+CqI+Cmr8nNEiHTs0XZE+IGku2emQnYThOW8Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:01:09.861779Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.08360","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1bd006f09222c92a508c044718f0269ad1b9cfab275d75727e7a70472e0e3346","sha256:45af6236ce058fbafcd797d5deae3c4205bc86cc93442b148c4548761e174679"],"state_sha256":"7168015392fb7e6148a2f84761a0d45f4b3becdfd76084efa0c488a56eead900"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6XkfoXoABbxLxuoNwI9l3sqNXmPLh2hQJFwmhGao3yZENl8FjM3rJp2kVrgSet9beHB/of3ojF/58fYm1JruBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T01:41:51.668764Z","bundle_sha256":"395aaf00b295da70451df492981decf1759215a6a417a789b915f385132f49b3"}}