{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Q6AAM54N5OQZHOBA6EAF5ACWDP","short_pith_number":"pith:Q6AAM54N","schema_version":"1.0","canonical_sha256":"878006778deba193b820f1005e80561bc08ecdbd580f53f274e019196acda9cb","source":{"kind":"arxiv","id":"2105.11720","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric classes for identification in random coefficients models when regressors have limited variation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Christophe Gaillac (TSE), Eric Gautier (TSE, UT1)","submitted_at":"2021-05-25T07:43:22Z","abstract_excerpt":"This paper studies point identification of the distribution of the coefficients in some random coefficients models with exogenous regressors when their support is a proper subset, possibly discrete but countable. We exhibit trade-offs between restrictions on the distribution of the random coefficients and the support of the regressors. We consider linear models including those with nonlinear transforms of a baseline regressor, with an infinite number of regressors and deconvolution, the binary choice model, and panel data models such as single-index panel data models and an extension of the Ko"},"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":"2105.11720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2021-05-25T07:43:22Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"bcd22d41b00b3bdec1d8d9015ba726ed70fce595c20d4973f9e541ce4b40403a","abstract_canon_sha256":"599f58e3dd3558aa285b6ca136a59325120ab3eb713753bc43e7f274cd1091be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:42:55.916832Z","signature_b64":"voTgT6C9JLHUe7riNig0vIDeeHjd180n7cE0H43WP1g09O4QpQa5WVSlQABwrdoVwnzrIyRFK8XAIEgsqf6FCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"878006778deba193b820f1005e80561bc08ecdbd580f53f274e019196acda9cb","last_reissued_at":"2026-07-05T02:42:55.916376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:42:55.916376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric classes for identification in random coefficients models when regressors have limited variation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Christophe Gaillac (TSE), Eric Gautier (TSE, UT1)","submitted_at":"2021-05-25T07:43:22Z","abstract_excerpt":"This paper studies point identification of the distribution of the coefficients in some random coefficients models with exogenous regressors when their support is a proper subset, possibly discrete but countable. We exhibit trade-offs between restrictions on the distribution of the random coefficients and the support of the regressors. We consider linear models including those with nonlinear transforms of a baseline regressor, with an infinite number of regressors and deconvolution, the binary choice model, and panel data models such as single-index panel data models and an extension of the Ko"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.11720","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.11720/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2105.11720","created_at":"2026-07-05T02:42:55.916436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.11720v1","created_at":"2026-07-05T02:42:55.916436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.11720","created_at":"2026-07-05T02:42:55.916436+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q6AAM54N5OQZ","created_at":"2026-07-05T02:42:55.916436+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q6AAM54N5OQZHOBA","created_at":"2026-07-05T02:42:55.916436+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q6AAM54N","created_at":"2026-07-05T02:42:55.916436+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.04654","citing_title":"A sliced Wasserstein and diffusion approach to random coefficient models","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP","json":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP.json","graph_json":"https://pith.science/api/pith-number/Q6AAM54N5OQZHOBA6EAF5ACWDP/graph.json","events_json":"https://pith.science/api/pith-number/Q6AAM54N5OQZHOBA6EAF5ACWDP/events.json","paper":"https://pith.science/paper/Q6AAM54N"},"agent_actions":{"view_html":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP","download_json":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP.json","view_paper":"https://pith.science/paper/Q6AAM54N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.11720&json=true","fetch_graph":"https://pith.science/api/pith-number/Q6AAM54N5OQZHOBA6EAF5ACWDP/graph.json","fetch_events":"https://pith.science/api/pith-number/Q6AAM54N5OQZHOBA6EAF5ACWDP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP/action/storage_attestation","attest_author":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP/action/author_attestation","sign_citation":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP/action/citation_signature","submit_replication":"https://pith.science/pith/Q6AAM54N5OQZHOBA6EAF5ACWDP/action/replication_record"}},"created_at":"2026-07-05T02:42:55.916436+00:00","updated_at":"2026-07-05T02:42:55.916436+00:00"}