{"id":"W2009687799","doi":"10.1002/cjs.11220","title":"Bayesian sensitivity analyses for hidden sub‐populations in weighted sampling","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Medical Expenditure Panel Survey; Statistics; Bayesian probability; Econometrics; Population; Sampling (signal processing); Sample (material); Sensitivity (control systems); Health care; Computer science; Mathematics; Medicine; Environmental health; Economics; Health insurance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.1792568,0.002030736,0.004101519,0.004724428,0.001281101,0.003864525,0.004464021,0.003482155,0.006077513],"category_scores_gemma":[0.4624389,0.002095247,0.005757268,0.002987126,0.005008545,0.005845445,0.006625858,0.006098718,0.0003235534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004083849,"about_ca_system_score_gemma":0.001782382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005386649,"about_ca_topic_score_gemma":0.002301485,"domain_scores_codex":[0.8351281,0.1512883,0.002514835,0.005077805,0.004698684,0.001292219],"domain_scores_gemma":[0.3856601,0.5803069,0.01240807,0.01639554,0.004423446,0.0008059094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006758573,0.0001904122,0.01005907,0.0009130237,0.003446938,0.0007174861,0.0007424359,0.5283083,0.0006955453,0.4058251,0.00224797,0.04617775],"study_design_scores_gemma":[0.0000903304,0.0001277665,0.00168,0.0001976259,0.0004699722,0.0001392105,0.00009441273,0.6367909,0.0004570387,0.3583928,0.001480176,0.00007984396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01556838,0.0007614121,0.9808072,0.0006241355,0.00007694443,0.0004082966,0.0002098962,0.0001408887,0.001402883],"genre_scores_gemma":[0.688023,0.001720557,0.302289,0.001257631,0.0003253313,0.003143459,0.0005894922,0.0002015825,0.002449924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1792568,"threshold_uncertainty_score":0.9480119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2100766677044757,"score_gpt":0.41858946097469,"score_spread":0.2085127932702143,"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."}}