{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:SEAI3WAUXM2DCGUKB5V2DRCBIM","short_pith_number":"pith:SEAI3WAU","canonical_record":{"source":{"id":"1609.02950","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-09-09T21:36:43Z","cross_cats_sorted":[],"title_canon_sha256":"09f5a7310374c14555f11d22fc9b5abca73b052d9aab2c89b6a01babd6f36da0","abstract_canon_sha256":"aed7d9204e9164aaf5b1fba03bbe5d9c626dc8cc39e6463ef07cce3b96541603"},"schema_version":"1.0"},"canonical_sha256":"91008dd814bb34311a8a0f6ba1c44143366ac82ae4145cb9f661567c4cf7c2ab","source":{"kind":"arxiv","id":"1609.02950","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.02950","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"1609.02950v1","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.02950","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"SEAI3WAUXM2D","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SEAI3WAUXM2DCGUK","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SEAI3WAU","created_at":"2026-05-18T12:30:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:SEAI3WAUXM2DCGUKB5V2DRCBIM","target":"record","payload":{"canonical_record":{"source":{"id":"1609.02950","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-09-09T21:36:43Z","cross_cats_sorted":[],"title_canon_sha256":"09f5a7310374c14555f11d22fc9b5abca73b052d9aab2c89b6a01babd6f36da0","abstract_canon_sha256":"aed7d9204e9164aaf5b1fba03bbe5d9c626dc8cc39e6463ef07cce3b96541603"},"schema_version":"1.0"},"canonical_sha256":"91008dd814bb34311a8a0f6ba1c44143366ac82ae4145cb9f661567c4cf7c2ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:23.706963Z","signature_b64":"95CzJuhTESRChzkVTcJbH4r+qiRg+hh8tph2iXTKrUShRjObH3rpUjVcoXarHdYXeBVmfaDsJXVsdWHKr81ACQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91008dd814bb34311a8a0f6ba1c44143366ac82ae4145cb9f661567c4cf7c2ab","last_reissued_at":"2026-05-18T00:01:23.706567Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:23.706567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.02950","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":"2026-05-18T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZSXOZH9Mc0wlHFWpq8cOi/8LPrNtbg68bz29Bcln2ZNaemSuzMsuPrh7EfxTOJJAgnIjbHAfV9qblP1t7MbnDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:55:44.546600Z"},"content_sha256":"1c9c4bde0f63eb77987d98e26f16ad69e31b5642fd6af324913b636e2bfabdcc","schema_version":"1.0","event_id":"sha256:1c9c4bde0f63eb77987d98e26f16ad69e31b5642fd6af324913b636e2bfabdcc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:SEAI3WAUXM2DCGUKB5V2DRCBIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Quantile Regression Using Random B-spline Series Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Priyam Das, Subhashis Ghoshal","submitted_at":"2016-09-09T21:36:43Z","abstract_excerpt":"We consider a Bayesian method for simultaneous quantile regression on a real variable. By monotone transformation, we can make both the response variable and the predictor variable take values in the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions $\\xi_1$ and $\\xi_2$ not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on $\\xi_1$ and $\\xi_2$. The monotonicity constraint on the curves $\\xi_1$ and $\\xi_2$ are obtained through a spline basis "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.02950","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":"2026-05-18T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eZka0jYBriGhZkbzWuSwlO7dJ7Ni7PXUainHIb8aWCg7WLo8Z33bRT2yKSDLo8E6AsTsmEVp/nt8mnRSmpYSCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T10:55:44.546944Z"},"content_sha256":"79d0055596a77bba36bb61ff0f0f3c678902a4d32d98a8dc52fcb569a4d47b98","schema_version":"1.0","event_id":"sha256:79d0055596a77bba36bb61ff0f0f3c678902a4d32d98a8dc52fcb569a4d47b98"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/bundle.json","state_url":"https://pith.science/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/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":"2026-06-05T10:55:44Z","links":{"resolver":"https://pith.science/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM","bundle":"https://pith.science/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/bundle.json","state":"https://pith.science/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SEAI3WAUXM2DCGUKB5V2DRCBIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:SEAI3WAUXM2DCGUKB5V2DRCBIM","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":"aed7d9204e9164aaf5b1fba03bbe5d9c626dc8cc39e6463ef07cce3b96541603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-09-09T21:36:43Z","title_canon_sha256":"09f5a7310374c14555f11d22fc9b5abca73b052d9aab2c89b6a01babd6f36da0"},"schema_version":"1.0","source":{"id":"1609.02950","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.02950","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"1609.02950v1","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.02950","created_at":"2026-05-18T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"SEAI3WAUXM2D","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SEAI3WAUXM2DCGUK","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SEAI3WAU","created_at":"2026-05-18T12:30:44Z"}],"graph_snapshots":[{"event_id":"sha256:79d0055596a77bba36bb61ff0f0f3c678902a4d32d98a8dc52fcb569a4d47b98","target":"graph","created_at":"2026-05-18T00:01:23Z","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 consider a Bayesian method for simultaneous quantile regression on a real variable. By monotone transformation, we can make both the response variable and the predictor variable take values in the unit interval. A representation of quantile function is given by a convex combination of two monotone increasing functions $\\xi_1$ and $\\xi_2$ not depending on the prediction variables. In a Bayesian approach, a prior is put on quantile functions by putting prior distributions on $\\xi_1$ and $\\xi_2$. The monotonicity constraint on the curves $\\xi_1$ and $\\xi_2$ are obtained through a spline basis ","authors_text":"Priyam Das, Subhashis Ghoshal","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-09-09T21:36:43Z","title":"Bayesian Quantile Regression Using Random B-spline Series Prior"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.02950","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":"sha256:1c9c4bde0f63eb77987d98e26f16ad69e31b5642fd6af324913b636e2bfabdcc","target":"record","created_at":"2026-05-18T00:01:23Z","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":"aed7d9204e9164aaf5b1fba03bbe5d9c626dc8cc39e6463ef07cce3b96541603","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-09-09T21:36:43Z","title_canon_sha256":"09f5a7310374c14555f11d22fc9b5abca73b052d9aab2c89b6a01babd6f36da0"},"schema_version":"1.0","source":{"id":"1609.02950","kind":"arxiv","version":1}},"canonical_sha256":"91008dd814bb34311a8a0f6ba1c44143366ac82ae4145cb9f661567c4cf7c2ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91008dd814bb34311a8a0f6ba1c44143366ac82ae4145cb9f661567c4cf7c2ab","first_computed_at":"2026-05-18T00:01:23.706567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:23.706567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"95CzJuhTESRChzkVTcJbH4r+qiRg+hh8tph2iXTKrUShRjObH3rpUjVcoXarHdYXeBVmfaDsJXVsdWHKr81ACQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:23.706963Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.02950","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c9c4bde0f63eb77987d98e26f16ad69e31b5642fd6af324913b636e2bfabdcc","sha256:79d0055596a77bba36bb61ff0f0f3c678902a4d32d98a8dc52fcb569a4d47b98"],"state_sha256":"b64f9dd942bdd91e61567effa67b89bfa17a021d0d4ab587acd0f19c4f8b34a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8pNEEn2lTO+qiTM58GA32jfDAxsyKY9dWyY/Z+8+t1R3nrc4BAagE6x1wQhHSjwlqfPx7koO0Ecv2+J/0rSBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T10:55:44.548890Z","bundle_sha256":"6d06fc73ac24ff5669dc95e4bb4dbbbebdaef3685e186e5e78e04a039dc37779"}}