{"id":"W2981024151","doi":"10.1002/cjs.11523","title":"Validity and efficiency in analyzing ordinal responses with missing observations","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Missing data; Imputation (statistics); Ordinal data; Covariate; Computer science; Ordinal regression; Statistics; Data set; Data mining; Econometrics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007100608,0.00009604399,0.0002592209,0.0002440464,0.0001238543,0.00006565692,0.00010191,0.00003822444,0.0001071236],"category_scores_gemma":[0.003166472,0.00007814637,0.00001502018,0.0002435581,0.000104794,0.00008996568,0.000008607051,0.000224586,0.000001191921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008905695,"about_ca_system_score_gemma":0.0009561706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000727969,"about_ca_topic_score_gemma":0.005455618,"domain_scores_codex":[0.9990129,0.0001492648,0.0003689252,0.00009966965,0.000142372,0.0002268032],"domain_scores_gemma":[0.9969591,0.002197682,0.0002003887,0.0001029601,0.0002260068,0.0003138934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006521477,0.00002609094,0.5280942,0.0001432834,0.00001734445,0.0005500467,0.00105967,0.0000125078,0.000140499,0.4581308,0.0004709488,0.01128942],"study_design_scores_gemma":[0.0006957483,0.0005153939,0.3660334,0.0005387392,0.00008140995,0.0003119834,0.000389187,0.002279102,0.00004359191,0.6283636,0.0004988922,0.0002488873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3431272,0.00003194581,0.6563027,0.0001518605,0.00006612089,0.00005462379,0.0001036943,0.000001687399,0.0001601874],"genre_scores_gemma":[0.4176728,0.000004516802,0.5822383,0.00002745804,0.0000160456,3.062785e-7,7.184821e-7,0.000007320903,0.00003256675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1702328,"threshold_uncertainty_score":0.3790791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.114010976247964,"score_gpt":0.3424375453341478,"score_spread":0.2284265690861838,"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."}}