{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HV3VSUUXTCK3AWPY6M362QQU4U","short_pith_number":"pith:HV3VSUUX","canonical_record":{"source":{"id":"1602.06609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-22T00:07:42Z","cross_cats_sorted":[],"title_canon_sha256":"245eaa6aebe547dfdd463d56dffe72131178ba5811b1e8d99dc114bc57fc1e68","abstract_canon_sha256":"381339746784933196d1ed666e7898ca91658e6b46d30a075c0e37db2f2e988d"},"schema_version":"1.0"},"canonical_sha256":"3d775952979895b059f8f337ed4214e50f49dcd46b9ba4e7cbc2b766ca17aa1b","source":{"kind":"arxiv","id":"1602.06609","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.06609","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"arxiv_version","alias_value":"1602.06609v1","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.06609","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"pith_short_12","alias_value":"HV3VSUUXTCK3","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"HV3VSUUXTCK3AWPY","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"HV3VSUUX","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HV3VSUUXTCK3AWPY6M362QQU4U","target":"record","payload":{"canonical_record":{"source":{"id":"1602.06609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-22T00:07:42Z","cross_cats_sorted":[],"title_canon_sha256":"245eaa6aebe547dfdd463d56dffe72131178ba5811b1e8d99dc114bc57fc1e68","abstract_canon_sha256":"381339746784933196d1ed666e7898ca91658e6b46d30a075c0e37db2f2e988d"},"schema_version":"1.0"},"canonical_sha256":"3d775952979895b059f8f337ed4214e50f49dcd46b9ba4e7cbc2b766ca17aa1b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:13.544156Z","signature_b64":"gb/eoPgeAw4t/IHUD/X7P7+viQ8ERXd9ODZ2krH64Z31nDFUGZXpsoN59wau6qMQ0zy45cn8KA4UForPVchhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d775952979895b059f8f337ed4214e50f49dcd46b9ba4e7cbc2b766ca17aa1b","last_reissued_at":"2026-05-18T01:20:13.543525Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:13.543525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.06609","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":"2026-05-18T01:20:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mjjNE3OUNpQHwnA06bIr3V4PqLNTqcSQQrLk1JmcaD+M52j2RzMcEfBuK441JJCXh/KzIi0dckAQxVVFz9s3Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T05:26:02.466955Z"},"content_sha256":"28d013e6a197590af21eabb5c12ef4fff349f96e8762a1afaf462bae78149607","schema_version":"1.0","event_id":"sha256:28d013e6a197590af21eabb5c12ef4fff349f96e8762a1afaf462bae78149607"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HV3VSUUXTCK3AWPY6M362QQU4U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Nonparametric and Varying Coefficient Modal Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Sijia Xiang, Weixin Yao","submitted_at":"2016-02-22T00:07:42Z","abstract_excerpt":"In this article, we propose a new nonparametric data analysis tool, which we call nonparametric modal regression, to investigate the relationship among interested variables based on estimating the mode of the conditional density of a response variable Y given predictors X. The nonparametric modal regression is distinguished from the conventional nonparametric regression in that, instead of the conditional average or median, it uses the \"most likely\" conditional values to measures the center. Better prediction performance and robustness are two important characteristics of nonparametric modal r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.06609","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":"2026-05-18T01:20:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DAH9MITnBB4fVNcP9QZSaoC9Wxs+ZdEwOWcm6P6hOpj+yMhTRIPA4DspS/AVVdNgTc2Y+pV1mmCjcGh4K/k0AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T05:26:02.467658Z"},"content_sha256":"79e5401f01deaca471a3c6bcd5bc4f7d353acfbb74209736c5e5848fba707593","schema_version":"1.0","event_id":"sha256:79e5401f01deaca471a3c6bcd5bc4f7d353acfbb74209736c5e5848fba707593"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HV3VSUUXTCK3AWPY6M362QQU4U/bundle.json","state_url":"https://pith.science/pith/HV3VSUUXTCK3AWPY6M362QQU4U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HV3VSUUXTCK3AWPY6M362QQU4U/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":"2026-06-09T05:26:02Z","links":{"resolver":"https://pith.science/pith/HV3VSUUXTCK3AWPY6M362QQU4U","bundle":"https://pith.science/pith/HV3VSUUXTCK3AWPY6M362QQU4U/bundle.json","state":"https://pith.science/pith/HV3VSUUXTCK3AWPY6M362QQU4U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HV3VSUUXTCK3AWPY6M362QQU4U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HV3VSUUXTCK3AWPY6M362QQU4U","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":"381339746784933196d1ed666e7898ca91658e6b46d30a075c0e37db2f2e988d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-22T00:07:42Z","title_canon_sha256":"245eaa6aebe547dfdd463d56dffe72131178ba5811b1e8d99dc114bc57fc1e68"},"schema_version":"1.0","source":{"id":"1602.06609","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.06609","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"arxiv_version","alias_value":"1602.06609v1","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.06609","created_at":"2026-05-18T01:20:13Z"},{"alias_kind":"pith_short_12","alias_value":"HV3VSUUXTCK3","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"HV3VSUUXTCK3AWPY","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"HV3VSUUX","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:79e5401f01deaca471a3c6bcd5bc4f7d353acfbb74209736c5e5848fba707593","target":"graph","created_at":"2026-05-18T01:20:13Z","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":"In this article, we propose a new nonparametric data analysis tool, which we call nonparametric modal regression, to investigate the relationship among interested variables based on estimating the mode of the conditional density of a response variable Y given predictors X. The nonparametric modal regression is distinguished from the conventional nonparametric regression in that, instead of the conditional average or median, it uses the \"most likely\" conditional values to measures the center. Better prediction performance and robustness are two important characteristics of nonparametric modal r","authors_text":"Sijia Xiang, Weixin Yao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-22T00:07:42Z","title":"Nonparametric and Varying Coefficient Modal Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.06609","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":"sha256:28d013e6a197590af21eabb5c12ef4fff349f96e8762a1afaf462bae78149607","target":"record","created_at":"2026-05-18T01:20:13Z","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":"381339746784933196d1ed666e7898ca91658e6b46d30a075c0e37db2f2e988d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-22T00:07:42Z","title_canon_sha256":"245eaa6aebe547dfdd463d56dffe72131178ba5811b1e8d99dc114bc57fc1e68"},"schema_version":"1.0","source":{"id":"1602.06609","kind":"arxiv","version":1}},"canonical_sha256":"3d775952979895b059f8f337ed4214e50f49dcd46b9ba4e7cbc2b766ca17aa1b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d775952979895b059f8f337ed4214e50f49dcd46b9ba4e7cbc2b766ca17aa1b","first_computed_at":"2026-05-18T01:20:13.543525Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:13.543525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gb/eoPgeAw4t/IHUD/X7P7+viQ8ERXd9ODZ2krH64Z31nDFUGZXpsoN59wau6qMQ0zy45cn8KA4UForPVchhCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:13.544156Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.06609","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28d013e6a197590af21eabb5c12ef4fff349f96e8762a1afaf462bae78149607","sha256:79e5401f01deaca471a3c6bcd5bc4f7d353acfbb74209736c5e5848fba707593"],"state_sha256":"6c549faaba808205288cc3f2ec6583542c0017c34da40e4884c9f92fe2840135"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d+U7ebpluVrzvCcPBkskjmiUhTVqXmJLsj1MoXnU2fTxxIrYjBpilZF66DIdkuPXIwMEyHKKLbz/tOwCO2BVBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T05:26:02.471798Z","bundle_sha256":"606ddd37c6167579050a7bfe668fb3196556ae53ad46eaddd7bb1f28a228f46f"}}