{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:MFWNCCAL2ZWKQVHMSRS6OQ2CNU","short_pith_number":"pith:MFWNCCAL","canonical_record":{"source":{"id":"1504.05796","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2015-04-22T13:32:10Z","cross_cats_sorted":[],"title_canon_sha256":"d8537cfb706070bcecb0a8876e55c0204bd4bc166af5b310d3523d88bbe28c31","abstract_canon_sha256":"30a271e078ff44300c84ea19b9672dcaf02eadc258649b1c2a1bb060ef767003"},"schema_version":"1.0"},"canonical_sha256":"616cd1080bd66ca854ec9465e743426d1e20fe75dadca6fcc644c3b512765f98","source":{"kind":"arxiv","id":"1504.05796","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.05796","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"arxiv_version","alias_value":"1504.05796v4","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.05796","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"pith_short_12","alias_value":"MFWNCCAL2ZWK","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_16","alias_value":"MFWNCCAL2ZWKQVHM","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_8","alias_value":"MFWNCCAL","created_at":"2026-05-18T12:29:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:MFWNCCAL2ZWKQVHMSRS6OQ2CNU","target":"record","payload":{"canonical_record":{"source":{"id":"1504.05796","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2015-04-22T13:32:10Z","cross_cats_sorted":[],"title_canon_sha256":"d8537cfb706070bcecb0a8876e55c0204bd4bc166af5b310d3523d88bbe28c31","abstract_canon_sha256":"30a271e078ff44300c84ea19b9672dcaf02eadc258649b1c2a1bb060ef767003"},"schema_version":"1.0"},"canonical_sha256":"616cd1080bd66ca854ec9465e743426d1e20fe75dadca6fcc644c3b512765f98","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:33:32.183603Z","signature_b64":"7rGsz6RI6i9ZCtByZVg2mTHGNm0XmvYTPBNCsfEBIN8jISe2xJBUB2VzedGvv9tK+M1PQN0Nymvg78QrbKQyAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"616cd1080bd66ca854ec9465e743426d1e20fe75dadca6fcc644c3b512765f98","last_reissued_at":"2026-05-18T01:33:32.182859Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:33:32.182859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.05796","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:33:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7jYUcV9061D7rdU+LydBEplmVQUiUIArrH/U6SxD/1Z08BX1y6vMY20mT1t8fgqLkM+HFuIgrmmkp81cdwWXAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:33:24.723204Z"},"content_sha256":"5ef8e887e6179118e49c4b744d6c98131b665efa94081fa57ddbcc323665cebf","schema_version":"1.0","event_id":"sha256:5ef8e887e6179118e49c4b744d6c98131b665efa94081fa57ddbcc323665cebf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:MFWNCCAL2ZWKQVHMSRS6OQ2CNU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Health risk modelling by transforming a multi-dimensional unknown distribution to a multi-dimensional Gaussian","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.data-an","authors_text":"V. Kapoor","submitted_at":"2015-04-22T13:32:10Z","abstract_excerpt":"The traditional approach of health risk modelling with multiple data sources proceeds via regression-based methods assuming a marginal distribution for the outcome variable. The data is collected for $N$ subjects over a $J$ time-period or from $J$ data sources. The response obtained from $i^{th}$ subject is $\\vec{Y}_i=({Y}_{i1},\\cdots, {Y}_{iJ})$. For $N$ subjects we obtain a $J$ dimensional joint distribution for the subjects. In this work we propose a novel approach of transforming any $J$ dimensional joint distribution to that of a $J$ dimensional Gaussian keeping the Shannon entropy consta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.05796","kind":"arxiv","version":4},"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":"2026-05-18T01:33:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mONzcZsGNHRoBKOWLZ+NxzYwc+JGYERo+rzo60BjuTrj8z2dpEBm1PWM+9fK7PCCY/ZZO7fgj2ME7wJ13PviBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T08:33:24.723559Z"},"content_sha256":"c01320d0a28db2d51af2f3a67bc9120eecd1e834b2a18210d731c3c4db475d9e","schema_version":"1.0","event_id":"sha256:c01320d0a28db2d51af2f3a67bc9120eecd1e834b2a18210d731c3c4db475d9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/bundle.json","state_url":"https://pith.science/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/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":"2026-06-12T08:33:24Z","links":{"resolver":"https://pith.science/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU","bundle":"https://pith.science/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/bundle.json","state":"https://pith.science/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MFWNCCAL2ZWKQVHMSRS6OQ2CNU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:MFWNCCAL2ZWKQVHMSRS6OQ2CNU","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":"30a271e078ff44300c84ea19b9672dcaf02eadc258649b1c2a1bb060ef767003","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2015-04-22T13:32:10Z","title_canon_sha256":"d8537cfb706070bcecb0a8876e55c0204bd4bc166af5b310d3523d88bbe28c31"},"schema_version":"1.0","source":{"id":"1504.05796","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.05796","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"arxiv_version","alias_value":"1504.05796v4","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.05796","created_at":"2026-05-18T01:33:32Z"},{"alias_kind":"pith_short_12","alias_value":"MFWNCCAL2ZWK","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_16","alias_value":"MFWNCCAL2ZWKQVHM","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_8","alias_value":"MFWNCCAL","created_at":"2026-05-18T12:29:32Z"}],"graph_snapshots":[{"event_id":"sha256:c01320d0a28db2d51af2f3a67bc9120eecd1e834b2a18210d731c3c4db475d9e","target":"graph","created_at":"2026-05-18T01:33:32Z","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":"The traditional approach of health risk modelling with multiple data sources proceeds via regression-based methods assuming a marginal distribution for the outcome variable. The data is collected for $N$ subjects over a $J$ time-period or from $J$ data sources. The response obtained from $i^{th}$ subject is $\\vec{Y}_i=({Y}_{i1},\\cdots, {Y}_{iJ})$. For $N$ subjects we obtain a $J$ dimensional joint distribution for the subjects. In this work we propose a novel approach of transforming any $J$ dimensional joint distribution to that of a $J$ dimensional Gaussian keeping the Shannon entropy consta","authors_text":"V. Kapoor","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2015-04-22T13:32:10Z","title":"Health risk modelling by transforming a multi-dimensional unknown distribution to a multi-dimensional Gaussian"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.05796","kind":"arxiv","version":4},"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":"sha256:5ef8e887e6179118e49c4b744d6c98131b665efa94081fa57ddbcc323665cebf","target":"record","created_at":"2026-05-18T01:33:32Z","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":"30a271e078ff44300c84ea19b9672dcaf02eadc258649b1c2a1bb060ef767003","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2015-04-22T13:32:10Z","title_canon_sha256":"d8537cfb706070bcecb0a8876e55c0204bd4bc166af5b310d3523d88bbe28c31"},"schema_version":"1.0","source":{"id":"1504.05796","kind":"arxiv","version":4}},"canonical_sha256":"616cd1080bd66ca854ec9465e743426d1e20fe75dadca6fcc644c3b512765f98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"616cd1080bd66ca854ec9465e743426d1e20fe75dadca6fcc644c3b512765f98","first_computed_at":"2026-05-18T01:33:32.182859Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:33:32.182859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7rGsz6RI6i9ZCtByZVg2mTHGNm0XmvYTPBNCsfEBIN8jISe2xJBUB2VzedGvv9tK+M1PQN0Nymvg78QrbKQyAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:33:32.183603Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.05796","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ef8e887e6179118e49c4b744d6c98131b665efa94081fa57ddbcc323665cebf","sha256:c01320d0a28db2d51af2f3a67bc9120eecd1e834b2a18210d731c3c4db475d9e"],"state_sha256":"6cb6c3e3ce39a7876e6669051defad43c848dc8a1ddc000d90f7280595710bd3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7+cLS5IR9Q75hA5FmZr+tAYZcnDvHzEZHQuUMXgFErNsO847Xl9GQaRHX2bvOLhVcbvS9J5F7qoFB786VUBbCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T08:33:24.725527Z","bundle_sha256":"1757eedea6b40049e72949395bd1ae888b31f35d815df467a685d1aa11f58c21"}}