{"id":"W2027581521","doi":"10.1002/cjs.11142","title":"Imputation for statistical inference with coarse data","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Conservation Service; Korea Labor Institute; Iowa State University; U.S. Department of Agriculture","keywords":"Missing data; Imputation (statistics); Estimator; Statistics; Likelihood function; Statistical inference; Mathematics; Inference; Maximum likelihood; Parametric statistics; Estimating equations; Monte Carlo method; Longitudinal data; Computer science; Econometrics; Applied mathematics; Data mining; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03364256,0.0009987972,0.003218338,0.003114308,0.001104349,0.002967348,0.003333547,0.002067955,0.005984189],"category_scores_gemma":[0.1493345,0.001101511,0.002251596,0.005953284,0.002885919,0.002963429,0.00497553,0.005466049,0.001015963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001867751,"about_ca_system_score_gemma":0.003368072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00438346,"about_ca_topic_score_gemma":0.004002414,"domain_scores_codex":[0.9690345,0.02393743,0.001336518,0.002303408,0.002923066,0.000465006],"domain_scores_gemma":[0.8670385,0.1056063,0.005459153,0.01821423,0.003118828,0.0005629459],"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":[0.0001688818,0.00008677616,0.006667381,0.0008939998,0.0009400927,0.0006796565,0.0004813587,0.2455748,0.0005457167,0.508158,0.01105504,0.2247483],"study_design_scores_gemma":[0.00003330288,0.00002579924,0.0008173076,0.0001307807,0.00005881472,0.0001261939,0.0000320457,0.4333907,0.0002505773,0.5610172,0.004090193,0.00002707618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001144534,0.0002778137,0.9976249,0.0002383297,0.00005106377,0.00003539287,0.0001123232,0.0001203005,0.0003953505],"genre_scores_gemma":[0.1570167,0.0007891129,0.8391277,0.000340012,0.0002344874,0.0006139853,0.0006080174,0.00013086,0.001139081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03364256,"threshold_uncertainty_score":0.177921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2988263049760236,"score_gpt":0.4052969619800268,"score_spread":0.1064706570040032,"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."}}