{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BQQPOSZTWHP5X5VDIDRVHUONO5","short_pith_number":"pith:BQQPOSZT","canonical_record":{"source":{"id":"1905.01455","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-04T08:18:44Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"5f4128963a3738e08980f3fed4ad1db6250694c19f7753657ea74afd80ceaacc","abstract_canon_sha256":"e1081f41288173bfe3e63adc4ae0d0c5440258fa35e985ab93fbc9103c075c85"},"schema_version":"1.0"},"canonical_sha256":"0c20f74b33b1dfdbf6a340e353d1cd774bbf569a6a02804b7bc4e0cfd84dbc32","source":{"kind":"arxiv","id":"1905.01455","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01455","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01455v1","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01455","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"pith_short_12","alias_value":"BQQPOSZTWHP5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BQQPOSZTWHP5X5VD","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BQQPOSZT","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BQQPOSZTWHP5X5VDIDRVHUONO5","target":"record","payload":{"canonical_record":{"source":{"id":"1905.01455","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-04T08:18:44Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"5f4128963a3738e08980f3fed4ad1db6250694c19f7753657ea74afd80ceaacc","abstract_canon_sha256":"e1081f41288173bfe3e63adc4ae0d0c5440258fa35e985ab93fbc9103c075c85"},"schema_version":"1.0"},"canonical_sha256":"0c20f74b33b1dfdbf6a340e353d1cd774bbf569a6a02804b7bc4e0cfd84dbc32","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:58.500417Z","signature_b64":"selbmLHexJQY20R8jObJmlvcR+XuKkaOvoKL6ZqYM7JbrPsxrZiYpe5ywEqzZVWdSSeDouQVgTpb6WORimbmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c20f74b33b1dfdbf6a340e353d1cd774bbf569a6a02804b7bc4e0cfd84dbc32","last_reissued_at":"2026-05-17T23:46:58.499681Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:58.499681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.01455","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":"2026-05-17T23:46:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+lrgSYMcKIyGDdfis6IrMATc4lH09JmkJSq6Fj/D5N5vHNP2Z9jSnkG1aLu0ObHv+qhvOw6M4DohhemhWnfSBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T20:45:31.850408Z"},"content_sha256":"62bb2153a146f09ee250b0764081fbcab2ffaa35bb8ecc02ecc30cd60540dfa5","schema_version":"1.0","event_id":"sha256:62bb2153a146f09ee250b0764081fbcab2ffaa35bb8ecc02ecc30cd60540dfa5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BQQPOSZTWHP5X5VDIDRVHUONO5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regularized estimation for highly multivariate log Gaussian Cox processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-Fran\\c{c}ois Coeurjolly, Rasmus Waagepetersen","submitted_at":"2019-05-04T08:18:44Z","abstract_excerpt":"Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01455","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":"2026-05-17T23:46:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lNHzROvgNcBhV+OuQDZdeQMrkXA2BGoFxEzrB3nOOTXUhaNm1HoB/FCC3+nVxwBXWbmZkhnLyJ9yEgMVDP9aBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T20:45:31.850825Z"},"content_sha256":"7d123b30f45dbebf303223cf1cea4dc0f7dcb3c43bb0dc014f24221b1600554c","schema_version":"1.0","event_id":"sha256:7d123b30f45dbebf303223cf1cea4dc0f7dcb3c43bb0dc014f24221b1600554c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/bundle.json","state_url":"https://pith.science/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/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":"2026-05-22T20:45:31Z","links":{"resolver":"https://pith.science/pith/BQQPOSZTWHP5X5VDIDRVHUONO5","bundle":"https://pith.science/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/bundle.json","state":"https://pith.science/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BQQPOSZTWHP5X5VDIDRVHUONO5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BQQPOSZTWHP5X5VDIDRVHUONO5","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":"e1081f41288173bfe3e63adc4ae0d0c5440258fa35e985ab93fbc9103c075c85","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-04T08:18:44Z","title_canon_sha256":"5f4128963a3738e08980f3fed4ad1db6250694c19f7753657ea74afd80ceaacc"},"schema_version":"1.0","source":{"id":"1905.01455","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.01455","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"arxiv_version","alias_value":"1905.01455v1","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01455","created_at":"2026-05-17T23:46:58Z"},{"alias_kind":"pith_short_12","alias_value":"BQQPOSZTWHP5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BQQPOSZTWHP5X5VD","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BQQPOSZT","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:7d123b30f45dbebf303223cf1cea4dc0f7dcb3c43bb0dc014f24221b1600554c","target":"graph","created_at":"2026-05-17T23:46:58Z","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":"Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.","authors_text":"Achmad Choiruddin, Francisco Cuevas-Pacheco, Jean-Fran\\c{c}ois Coeurjolly, Rasmus Waagepetersen","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-04T08:18:44Z","title":"Regularized estimation for highly multivariate log Gaussian Cox processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01455","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":"sha256:62bb2153a146f09ee250b0764081fbcab2ffaa35bb8ecc02ecc30cd60540dfa5","target":"record","created_at":"2026-05-17T23:46:58Z","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":"e1081f41288173bfe3e63adc4ae0d0c5440258fa35e985ab93fbc9103c075c85","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-04T08:18:44Z","title_canon_sha256":"5f4128963a3738e08980f3fed4ad1db6250694c19f7753657ea74afd80ceaacc"},"schema_version":"1.0","source":{"id":"1905.01455","kind":"arxiv","version":1}},"canonical_sha256":"0c20f74b33b1dfdbf6a340e353d1cd774bbf569a6a02804b7bc4e0cfd84dbc32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0c20f74b33b1dfdbf6a340e353d1cd774bbf569a6a02804b7bc4e0cfd84dbc32","first_computed_at":"2026-05-17T23:46:58.499681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:58.499681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"selbmLHexJQY20R8jObJmlvcR+XuKkaOvoKL6ZqYM7JbrPsxrZiYpe5ywEqzZVWdSSeDouQVgTpb6WORimbmAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:58.500417Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.01455","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62bb2153a146f09ee250b0764081fbcab2ffaa35bb8ecc02ecc30cd60540dfa5","sha256:7d123b30f45dbebf303223cf1cea4dc0f7dcb3c43bb0dc014f24221b1600554c"],"state_sha256":"d4fb689a8c62a3253611ba87db1170655deb13d65fa4052e4f7c345781c9f9e0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G8bVWHAX8NxdVIBVkULJ9w+RGSfx6FtUApYuo+rZmiv0RkITgGrBBA6FWUJLLNz/uHYMvBA83Dk1E2gfXZgCAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T20:45:31.853090Z","bundle_sha256":"529311140de9f1d5b4e924d2f8e197a371289318368fee5585674741c668267a"}}