{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:SF5KEMY5MEIKBU2B5FZVKWUOBG","short_pith_number":"pith:SF5KEMY5","canonical_record":{"source":{"id":"1105.1924","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-05-10T12:09:22Z","cross_cats_sorted":[],"title_canon_sha256":"d61bb4e2320885c24fc458cdd76d22d832a3ba889b08f672444f5691a08596a2","abstract_canon_sha256":"d811ca95e32278019827582c5e524f41408b568ef548ea73c521b7fe220938f8"},"schema_version":"1.0"},"canonical_sha256":"917aa2331d6110a0d341e973555a8e099fd1e448333054b324c9b3478cd7f293","source":{"kind":"arxiv","id":"1105.1924","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1105.1924","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"1105.1924v2","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1105.1924","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"SF5KEMY5MEIK","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"SF5KEMY5MEIKBU2B","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"SF5KEMY5","created_at":"2026-05-18T12:26:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:SF5KEMY5MEIKBU2B5FZVKWUOBG","target":"record","payload":{"canonical_record":{"source":{"id":"1105.1924","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-05-10T12:09:22Z","cross_cats_sorted":[],"title_canon_sha256":"d61bb4e2320885c24fc458cdd76d22d832a3ba889b08f672444f5691a08596a2","abstract_canon_sha256":"d811ca95e32278019827582c5e524f41408b568ef548ea73c521b7fe220938f8"},"schema_version":"1.0"},"canonical_sha256":"917aa2331d6110a0d341e973555a8e099fd1e448333054b324c9b3478cd7f293","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:08:24.221215Z","signature_b64":"FR+l+7+brgS8rM+3+0jTITfbVX+s7NIjtHhy3okU5vePiqQJGaJ/6frOEG6SZsta/c8TyMAAUuzU45UpALY9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"917aa2331d6110a0d341e973555a8e099fd1e448333054b324c9b3478cd7f293","last_reissued_at":"2026-05-18T04:08:24.220511Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:08:24.220511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1105.1924","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"92HvgEKFUzp8jVJXW4wizuuDoFe3r+nz59BViBvw6aHGwPbhbg7aRpxJ6vCviHZKnMdQH7SJKNhvd0Ls+y/VBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:04:35.795360Z"},"content_sha256":"71db255c2f34fc8b5df25dd0c9ed02e30c0f2fe1dbee6cd1f81d606c736f7658","schema_version":"1.0","event_id":"sha256:71db255c2f34fc8b5df25dd0c9ed02e30c0f2fe1dbee6cd1f81d606c736f7658"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:SF5KEMY5MEIKBU2B5FZVKWUOBG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multivariate convex regression with adaptive partitioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"David B. Dunson, Lauren A. Hannah","submitted_at":"2011-05-10T12:09:22Z","abstract_excerpt":"We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that is computationally feasible for more than a few hundred observations. We introduce Convex Adaptive Partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions. CAP is computationally efficient, in stark "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1105.1924","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I6QYrbMZSVvkEUfdozBdz9atoDNJvak39If7a0NiyJJ+aFCqmG8P0r0/NtQ/9aauut1N9lIvgpY4hiX1e5MaBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:04:35.795722Z"},"content_sha256":"e2af1b79b0dad4db0a13c0d86e9d7d5fa3a39841d2e457296016c8e84686835e","schema_version":"1.0","event_id":"sha256:e2af1b79b0dad4db0a13c0d86e9d7d5fa3a39841d2e457296016c8e84686835e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/bundle.json","state_url":"https://pith.science/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/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":"2026-05-30T09:04:35Z","links":{"resolver":"https://pith.science/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG","bundle":"https://pith.science/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/bundle.json","state":"https://pith.science/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SF5KEMY5MEIKBU2B5FZVKWUOBG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:SF5KEMY5MEIKBU2B5FZVKWUOBG","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":"d811ca95e32278019827582c5e524f41408b568ef548ea73c521b7fe220938f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-05-10T12:09:22Z","title_canon_sha256":"d61bb4e2320885c24fc458cdd76d22d832a3ba889b08f672444f5691a08596a2"},"schema_version":"1.0","source":{"id":"1105.1924","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1105.1924","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"1105.1924v2","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1105.1924","created_at":"2026-05-18T04:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"SF5KEMY5MEIK","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"SF5KEMY5MEIKBU2B","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"SF5KEMY5","created_at":"2026-05-18T12:26:41Z"}],"graph_snapshots":[{"event_id":"sha256:e2af1b79b0dad4db0a13c0d86e9d7d5fa3a39841d2e457296016c8e84686835e","target":"graph","created_at":"2026-05-18T04:08:24Z","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":"We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that is computationally feasible for more than a few hundred observations. We introduce Convex Adaptive Partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions. CAP is computationally efficient, in stark ","authors_text":"David B. Dunson, Lauren A. Hannah","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-05-10T12:09:22Z","title":"Multivariate convex regression with adaptive partitioning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1105.1924","kind":"arxiv","version":2},"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":"sha256:71db255c2f34fc8b5df25dd0c9ed02e30c0f2fe1dbee6cd1f81d606c736f7658","target":"record","created_at":"2026-05-18T04:08:24Z","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":"d811ca95e32278019827582c5e524f41408b568ef548ea73c521b7fe220938f8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-05-10T12:09:22Z","title_canon_sha256":"d61bb4e2320885c24fc458cdd76d22d832a3ba889b08f672444f5691a08596a2"},"schema_version":"1.0","source":{"id":"1105.1924","kind":"arxiv","version":2}},"canonical_sha256":"917aa2331d6110a0d341e973555a8e099fd1e448333054b324c9b3478cd7f293","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"917aa2331d6110a0d341e973555a8e099fd1e448333054b324c9b3478cd7f293","first_computed_at":"2026-05-18T04:08:24.220511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:08:24.220511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FR+l+7+brgS8rM+3+0jTITfbVX+s7NIjtHhy3okU5vePiqQJGaJ/6frOEG6SZsta/c8TyMAAUuzU45UpALY9Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T04:08:24.221215Z","signed_message":"canonical_sha256_bytes"},"source_id":"1105.1924","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71db255c2f34fc8b5df25dd0c9ed02e30c0f2fe1dbee6cd1f81d606c736f7658","sha256:e2af1b79b0dad4db0a13c0d86e9d7d5fa3a39841d2e457296016c8e84686835e"],"state_sha256":"1e5acf563a92e8ee2dbd6a5b576c5d269e57deb694f72a82081f805a7d68cc5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+HhkX6iHEEkIaTLsqAUUCaTOKW4Fem7ntH0CUqHTiCU6qniJbxdKpQB9hdHPKcf1/gUEomhr5NYaeTFxJalLCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T09:04:35.798475Z","bundle_sha256":"2667455f5106f854dc199a7ba89a3d17b66d7a6c74643d811a0ef414eb2b4dc6"}}