{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0002930134,0.0002430975,0.0002904518,0.0001594886,0.0001252249,0.0009333801,0.006433298,0.0001203999,0.0002129949],"category_scores_gemma":[0.02085659,0.0002220129,0.00003493372,0.0003286574,0.0004659628,0.0006246497,0.01899535,0.0003762379,0.0001598285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000769389,"about_ca_system_score_gemma":0.0001840055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000370672,"about_ca_topic_score_gemma":0.000005209897,"domain_scores_codex":[0.9976149,0.00016521,0.0004880297,0.0008781356,0.0004368821,0.0004168431],"domain_scores_gemma":[0.9934566,0.002895569,0.00007914883,0.003352775,0.00006606198,0.0001498519],"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":[0.00001070353,0.00004645957,0.00001922123,0.000135612,0.00005261796,0.000114518,0.0001144924,0.000004585813,0.0004236758,0.7586076,0.1032542,0.1372163],"study_design_scores_gemma":[0.0001429356,0.00009207625,0.001743693,0.000117911,0.00002328341,0.00002945037,0.000009370427,0.4601624,0.0002648344,0.5364738,0.000763891,0.0001763725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001960202,0.0002023516,0.9795771,0.01558757,0.000628019,0.0002212842,0.0004358529,0.001112132,0.0002755043],"genre_scores_gemma":[0.5378293,0.0000586686,0.4618628,0.0001124352,0.00002170065,0.00001697108,0.00005348026,0.00001441838,0.00003020405],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5358691,"threshold_uncertainty_score":0.9989424,"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."}}