{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LE6UZCQRONFTOCSD54G7YERNCD","short_pith_number":"pith:LE6UZCQR","canonical_record":{"source":{"id":"1801.07644","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-23T16:34:11Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"6778509117859724a4e00570d9ab536956d327650962f48e2428ee4771e23ded","abstract_canon_sha256":"44a93839d177b307f1825e45f15c6295887329084eafad4667c723e7db07456d"},"schema_version":"1.0"},"canonical_sha256":"593d4c8a11734b370a43ef0dfc122d10e4721994a8dc9e0c2cb7b7a1c0291b13","source":{"kind":"arxiv","id":"1801.07644","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.07644","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"arxiv_version","alias_value":"1801.07644v2","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.07644","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"pith_short_12","alias_value":"LE6UZCQRONFT","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LE6UZCQRONFTOCSD","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LE6UZCQR","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LE6UZCQRONFTOCSD54G7YERNCD","target":"record","payload":{"canonical_record":{"source":{"id":"1801.07644","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-23T16:34:11Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"6778509117859724a4e00570d9ab536956d327650962f48e2428ee4771e23ded","abstract_canon_sha256":"44a93839d177b307f1825e45f15c6295887329084eafad4667c723e7db07456d"},"schema_version":"1.0"},"canonical_sha256":"593d4c8a11734b370a43ef0dfc122d10e4721994a8dc9e0c2cb7b7a1c0291b13","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:07.078234Z","signature_b64":"lJrvbMlvmNpMxwrVdz4nFPycJgncoIYf1TdLdYWOeJWNZFRkgBUNE68P+MCziqtQcE8q9dPwbabpfry3H5KVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"593d4c8a11734b370a43ef0dfc122d10e4721994a8dc9e0c2cb7b7a1c0291b13","last_reissued_at":"2026-05-18T00:25:07.077627Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:07.077627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.07644","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":"2026-05-18T00:25:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a7Vg5gkaMnzyY6wwSsqkW6O7vGJYGLxEFD4MNN9l4fQkByIxQNgwMjAkZihqzdUbZkA9vUhnX8siAdzTyJiADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T13:26:57.056966Z"},"content_sha256":"abb83f2c0f0d8dc7e9d40e701c3b1b2dfed792d1f0249579cea10e960f495e31","schema_version":"1.0","event_id":"sha256:abb83f2c0f0d8dc7e9d40e701c3b1b2dfed792d1f0249579cea10e960f495e31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LE6UZCQRONFTOCSD54G7YERNCD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Non-parametric Sparse Additive Auto-regressive Network Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Garvesh Raskutti, Hao Henry Zhou","submitted_at":"2018-01-23T16:34:11Z","abstract_excerpt":"Consider a multi-variate time series $(X_t)_{t=0}^{T}$ where $X_t \\in \\mathbb{R}^d$ which may represent spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge associated with these time series models is to estimate an influence network between the $d$ variables, especially when the number of variables $d$ is large meaning we are in the high-dimensional setting. Prior work has focused on parametric vector auto-regressive models. However, parametric approaches are somewhat restrictive in practice. In this paper, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.07644","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":"2026-05-18T00:25:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SEgAjywmQ7pmEEvW/KwhouwT/2S1np35S0qO7z54kZ11ZYXhNW2B+HpwX/assu36zDdXpL5zlpqAcRq2vLwSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T13:26:57.057328Z"},"content_sha256":"56699f4c3fd73cd564bdf0c24457aa231857a403292b31c5599484b1cd43a6f3","schema_version":"1.0","event_id":"sha256:56699f4c3fd73cd564bdf0c24457aa231857a403292b31c5599484b1cd43a6f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LE6UZCQRONFTOCSD54G7YERNCD/bundle.json","state_url":"https://pith.science/pith/LE6UZCQRONFTOCSD54G7YERNCD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LE6UZCQRONFTOCSD54G7YERNCD/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":"2026-06-01T13:26:57Z","links":{"resolver":"https://pith.science/pith/LE6UZCQRONFTOCSD54G7YERNCD","bundle":"https://pith.science/pith/LE6UZCQRONFTOCSD54G7YERNCD/bundle.json","state":"https://pith.science/pith/LE6UZCQRONFTOCSD54G7YERNCD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LE6UZCQRONFTOCSD54G7YERNCD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LE6UZCQRONFTOCSD54G7YERNCD","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":"44a93839d177b307f1825e45f15c6295887329084eafad4667c723e7db07456d","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-23T16:34:11Z","title_canon_sha256":"6778509117859724a4e00570d9ab536956d327650962f48e2428ee4771e23ded"},"schema_version":"1.0","source":{"id":"1801.07644","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.07644","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"arxiv_version","alias_value":"1801.07644v2","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.07644","created_at":"2026-05-18T00:25:07Z"},{"alias_kind":"pith_short_12","alias_value":"LE6UZCQRONFT","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LE6UZCQRONFTOCSD","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LE6UZCQR","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:56699f4c3fd73cd564bdf0c24457aa231857a403292b31c5599484b1cd43a6f3","target":"graph","created_at":"2026-05-18T00:25:07Z","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":"Consider a multi-variate time series $(X_t)_{t=0}^{T}$ where $X_t \\in \\mathbb{R}^d$ which may represent spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge associated with these time series models is to estimate an influence network between the $d$ variables, especially when the number of variables $d$ is large meaning we are in the high-dimensional setting. Prior work has focused on parametric vector auto-regressive models. However, parametric approaches are somewhat restrictive in practice. In this paper, we","authors_text":"Garvesh Raskutti, Hao Henry Zhou","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-23T16:34:11Z","title":"Non-parametric Sparse Additive Auto-regressive Network Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.07644","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:abb83f2c0f0d8dc7e9d40e701c3b1b2dfed792d1f0249579cea10e960f495e31","target":"record","created_at":"2026-05-18T00:25:07Z","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":"44a93839d177b307f1825e45f15c6295887329084eafad4667c723e7db07456d","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-23T16:34:11Z","title_canon_sha256":"6778509117859724a4e00570d9ab536956d327650962f48e2428ee4771e23ded"},"schema_version":"1.0","source":{"id":"1801.07644","kind":"arxiv","version":2}},"canonical_sha256":"593d4c8a11734b370a43ef0dfc122d10e4721994a8dc9e0c2cb7b7a1c0291b13","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"593d4c8a11734b370a43ef0dfc122d10e4721994a8dc9e0c2cb7b7a1c0291b13","first_computed_at":"2026-05-18T00:25:07.077627Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:07.077627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lJrvbMlvmNpMxwrVdz4nFPycJgncoIYf1TdLdYWOeJWNZFRkgBUNE68P+MCziqtQcE8q9dPwbabpfry3H5KVBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:07.078234Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.07644","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abb83f2c0f0d8dc7e9d40e701c3b1b2dfed792d1f0249579cea10e960f495e31","sha256:56699f4c3fd73cd564bdf0c24457aa231857a403292b31c5599484b1cd43a6f3"],"state_sha256":"ca53075d529fbe206495ce3aebfdde2eed308d71b5c4c52a734426c8a1872217"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/pcdEjQ/t6gr0DQ8saUA/1gDXMDUV7tQTH4IJatsF4CpxGl98QD2j8cpO8KYLoYAdO7fjsEaAmwUFDwbXXpSAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T13:26:57.059287Z","bundle_sha256":"f78e4c323475094e9a473ba1e7c83ac914b83b4316807c37bd311a820ac7d4f6"}}