{"id":"W3208102029","doi":"10.5705/ss.202022.0276","title":"Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy","year":2024,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Statistics; Confidence interval; Differential privacy; Estimation; Unbiased Estimation; Computer science; Mathematics; Estimator; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02974859,0.001669388,0.00247604,0.004022528,0.001352879,0.004852844,0.003793112,0.00310739,0.002782981],"category_scores_gemma":[0.2573657,0.001408253,0.001990153,0.003542214,0.005417629,0.008339768,0.007555674,0.006142728,0.001313942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0016344,"about_ca_system_score_gemma":0.002294258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005983003,"about_ca_topic_score_gemma":0.0003357542,"domain_scores_codex":[0.9650027,0.01799944,0.001892365,0.004287892,0.009304385,0.001513309],"domain_scores_gemma":[0.7853481,0.1545354,0.01079908,0.03738279,0.01054218,0.001392471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005582944,0.0001118002,0.003768777,0.0003229741,0.0002391703,0.0006038746,0.0007441281,0.1608726,0.006955462,0.6702278,0.002775942,0.1528192],"study_design_scores_gemma":[0.00006988557,0.0001272442,0.0005261892,0.0001237506,0.00005407094,0.0005177948,0.00006576985,0.4515555,0.01293687,0.5305264,0.003422787,0.00007373738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002294336,0.0001108785,0.9965858,0.0001130569,0.00001449499,0.00002287908,0.00004857999,0.0001967458,0.0006131667],"genre_scores_gemma":[0.3435741,0.0006234078,0.6519504,0.0005713022,0.0002479134,0.0005592745,0.0005387277,0.0004577514,0.001477116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02974859,"threshold_uncertainty_score":0.1573275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05127638862177194,"score_gpt":0.3524253205251359,"score_spread":0.301148931903364,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}