{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:2LBXBR6LFZIBWDW2KK3DODGGOC","short_pith_number":"pith:2LBXBR6L","schema_version":"1.0","canonical_sha256":"d2c370c7cb2e501b0eda52b6370cc6709b7b3b168f33ab6a4b4dbf0e22341d63","source":{"kind":"arxiv","id":"1605.04281","version":1},"attestation_state":"computed","paper":{"title":"Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Andreas Fuest, Andreas Mayr, Sarah Brockhaus, Sonja Greven","submitted_at":"2016-05-13T18:50:11Z","abstract_excerpt":"We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We thus combine signal regression models with generalized additive models for location, scale and shape (GAMLSS). We compare two fundamentally different methods for estimation, a gradient boosting and a penalized likelihood based approach, and address practically important points like identifiability and model choice. Estimation by a component-wise gradient boosting algorithm allows for high dimensional data settings and variable selection. Estimatio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1605.04281","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-05-13T18:50:11Z","cross_cats_sorted":[],"title_canon_sha256":"5f29ed08c8c53a3e680fc738e879c10c518bb6a4915a47bd1d1bcbcbfb41fe6f","abstract_canon_sha256":"9d8e927b479a58c05fa14e190f9f4c94a8686ffe9321f5d867bdf1187e8fb8dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:53.833925Z","signature_b64":"a3J6mdaUY6xU+AnbKHWMVXsd8eNZoVd9qCFRTGyfn2IN+0eQrGLVxJ8hvk02fggwQgewYM+6XjUBJ5S8sz+xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2c370c7cb2e501b0eda52b6370cc6709b7b3b168f33ab6a4b4dbf0e22341d63","last_reissued_at":"2026-05-18T01:14:53.833406Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:53.833406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Andreas Fuest, Andreas Mayr, Sarah Brockhaus, Sonja Greven","submitted_at":"2016-05-13T18:50:11Z","abstract_excerpt":"We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We thus combine signal regression models with generalized additive models for location, scale and shape (GAMLSS). We compare two fundamentally different methods for estimation, a gradient boosting and a penalized likelihood based approach, and address practically important points like identifiability and model choice. Estimation by a component-wise gradient boosting algorithm allows for high dimensional data settings and variable selection. Estimatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.04281","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1605.04281","created_at":"2026-05-18T01:14:53.833480+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.04281v1","created_at":"2026-05-18T01:14:53.833480+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.04281","created_at":"2026-05-18T01:14:53.833480+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LBXBR6LFZIB","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LBXBR6LFZIBWDW2","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LBXBR6L","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC","json":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC.json","graph_json":"https://pith.science/api/pith-number/2LBXBR6LFZIBWDW2KK3DODGGOC/graph.json","events_json":"https://pith.science/api/pith-number/2LBXBR6LFZIBWDW2KK3DODGGOC/events.json","paper":"https://pith.science/paper/2LBXBR6L"},"agent_actions":{"view_html":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC","download_json":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC.json","view_paper":"https://pith.science/paper/2LBXBR6L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.04281&json=true","fetch_graph":"https://pith.science/api/pith-number/2LBXBR6LFZIBWDW2KK3DODGGOC/graph.json","fetch_events":"https://pith.science/api/pith-number/2LBXBR6LFZIBWDW2KK3DODGGOC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC/action/storage_attestation","attest_author":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC/action/author_attestation","sign_citation":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC/action/citation_signature","submit_replication":"https://pith.science/pith/2LBXBR6LFZIBWDW2KK3DODGGOC/action/replication_record"}},"created_at":"2026-05-18T01:14:53.833480+00:00","updated_at":"2026-05-18T01:14:53.833480+00:00"}