{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007257086,0.000169086,0.0003398395,0.0003036763,0.00007822527,0.0000681126,0.00026214,0.00007423659,0.00001001449],"category_scores_gemma":[0.005138076,0.0001527856,0.00003145869,0.0002394779,0.0001018641,0.0002162468,0.00001030824,0.0002574192,0.000007197866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005033852,"about_ca_system_score_gemma":0.002082292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001668152,"about_ca_topic_score_gemma":0.002549234,"domain_scores_codex":[0.9985746,0.0001183694,0.0005192182,0.0001419111,0.0003053899,0.0003405271],"domain_scores_gemma":[0.9960606,0.000453214,0.0002855889,0.0002176937,0.001547781,0.001435139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000162699,0.0005850342,0.02160878,0.0004632445,0.0003411614,0.001063628,0.03006631,0.006637016,0.0003839681,0.1425566,0.7572769,0.03885464],"study_design_scores_gemma":[0.0007204555,0.002561918,0.001267038,0.0001824297,0.0001600298,0.000183064,0.003915651,0.006751155,0.0006819546,0.9802959,0.002772111,0.0005083233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02084249,0.0001094052,0.9775917,0.0003352849,0.0001750073,0.0002186632,0.0002134905,0.00003646257,0.0004775193],"genre_scores_gemma":[0.3850249,0.000002144905,0.6144316,0.0003944855,0.00009305101,0.000005682373,0.00001059375,0.00002521783,0.00001233567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8377392,"threshold_uncertainty_score":0.6230417,"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."}}