{"id":"W3126006076","doi":"","title":"How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Missing data; Categorical variable; Imputation (statistics); Bayesian probability; Computer science; Statistics; Data mining; Regression; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2629628,0.001586371,0.004013374,0.003456311,0.001556031,0.003492665,0.004582939,0.004329272,0.008951067],"category_scores_gemma":[0.6768916,0.0009774808,0.004410842,0.003668936,0.005737149,0.009177403,0.00398193,0.004799048,0.001421143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373684,"about_ca_system_score_gemma":0.004273352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002139821,"about_ca_topic_score_gemma":0.0009447828,"domain_scores_codex":[0.7999559,0.173465,0.004423805,0.008233927,0.01232352,0.001597748],"domain_scores_gemma":[0.1172708,0.8515968,0.007710186,0.01679445,0.005463593,0.001164088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008318393,0.001483864,0.06959204,0.002010055,0.009222347,0.0009257392,0.003063293,0.1816717,0.00199965,0.2649704,0.01144467,0.4452979],"study_design_scores_gemma":[0.001361031,0.002009614,0.01761821,0.0004914543,0.001095371,0.0005398965,0.0007776011,0.6836773,0.002298862,0.2852585,0.004656888,0.000215268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08358679,0.0006878251,0.9077731,0.002527578,0.0001460631,0.0007441207,0.000329003,0.0004834684,0.003722036],"genre_scores_gemma":[0.5370094,0.0006043268,0.456214,0.001182239,0.0002724621,0.001664912,0.0009115051,0.0004802912,0.001660971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2629628,"threshold_uncertainty_score":0.908898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04866115049043223,"score_gpt":0.3706269176177021,"score_spread":0.3219657671272699,"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."}}