{"id":"W1556389145","doi":"10.1002/cjs.11254","title":"An imputation based empirical likelihood approach to pretest–posttest studies","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Confidence interval; Imputation (statistics); Statistics; Missing data; Treatment effect; Empirical likelihood; Type I and type II errors; Parametric statistics; Statistical hypothesis testing; Treatment and control groups; Mathematics; Econometrics; Computer science; Psychology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08709645,0.001441346,0.003827768,0.005444308,0.0009630453,0.00344171,0.009362919,0.004084214,0.01019752],"category_scores_gemma":[0.2247332,0.001571499,0.003332017,0.007601508,0.00230868,0.003615774,0.003121275,0.006090861,0.001706564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002407218,"about_ca_system_score_gemma":0.003584178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005209178,"about_ca_topic_score_gemma":0.004176981,"domain_scores_codex":[0.9321277,0.05906589,0.001933965,0.0028548,0.003412666,0.0006050252],"domain_scores_gemma":[0.77101,0.2028195,0.008348412,0.01032258,0.006599164,0.0009002593],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004540338,0.000458712,0.01462929,0.001619937,0.00177247,0.001082529,0.001722211,0.1893385,0.0004134091,0.4694851,0.008691618,0.3103322],"study_design_scores_gemma":[0.000197839,0.0002656184,0.003089205,0.0003729974,0.0002751489,0.0002508618,0.0002088039,0.5243663,0.0004541546,0.4615407,0.00890099,0.00007747138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00137545,0.0004165581,0.9964359,0.0004159722,0.00005137397,0.0002181342,0.0002125547,0.0001777855,0.0006963663],"genre_scores_gemma":[0.1329533,0.001131291,0.857973,0.0005345053,0.0004127218,0.002700969,0.001081203,0.0001826462,0.003030285],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9129035,"threshold_uncertainty_score":0.4606155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3289284696619818,"score_gpt":0.4521218722124962,"score_spread":0.1231934025505144,"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."}}