{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001030517,0.0001508895,0.0003233691,0.0001242962,0.0001215442,0.00007414778,0.0003556893,0.00006221081,0.0002812665],"category_scores_gemma":[0.01269892,0.0001180853,0.00001729978,0.0001113155,0.000197535,0.0002362384,0.00001985636,0.0002147,0.000006910178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000887571,"about_ca_system_score_gemma":0.001323155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004119411,"about_ca_topic_score_gemma":0.002922589,"domain_scores_codex":[0.9985208,0.00009870562,0.0005307375,0.0001270142,0.0002414892,0.0004812565],"domain_scores_gemma":[0.9926881,0.005090788,0.0003425794,0.0003010909,0.0006166078,0.0009609019],"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":[0.00004243063,0.00003569667,0.003345079,0.0001024909,0.00005496683,0.00004137542,0.0003328048,0.000006127121,0.000006656624,0.9111263,0.03393583,0.05097022],"study_design_scores_gemma":[0.00130919,0.0009967021,0.00978558,0.0001844698,0.0004874113,0.0002821002,0.0004727399,0.01257424,0.00004470368,0.9547912,0.01858746,0.0004841632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001556792,0.00006948891,0.9904176,0.00008970017,0.0003575138,0.0001708071,0.007046611,0.000005206209,0.0002863105],"genre_scores_gemma":[0.1985592,0.000005388179,0.8010148,0.00008851728,0.0001943199,0.000003146657,0.00008118024,0.00002340238,0.00003006281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1970024,"threshold_uncertainty_score":0.9956175,"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."}}