{"id":"W4249957214","doi":"10.22215/etd/2014-10095","title":"Semiparametric Marginal Models For Incomplete Binary Longitudinal Data With Dropouts","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Marginal model; Estimator; Generalized estimating equation; Mathematics; Estimating equations; Kernel smoother; Smoothing; Generalized linear model; Statistics; Semiparametric model; Linear model; Applied mathematics; Regression analysis; Computer science; Kernel method; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007034807,0.0004212236,0.0007756426,0.0002522948,0.0001300835,0.00009711656,0.0008363926,0.0002558297,0.0003303076],"category_scores_gemma":[0.001638678,0.000303469,0.00007092192,0.0003153163,0.00005433125,0.0001477704,0.000108588,0.0003227454,0.00002108319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004031648,"about_ca_system_score_gemma":0.0001632432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008236869,"about_ca_topic_score_gemma":0.0001594488,"domain_scores_codex":[0.9977332,0.00009316111,0.0005130885,0.0007986958,0.0004605957,0.0004012706],"domain_scores_gemma":[0.9945018,0.00346895,0.0003430162,0.001207716,0.000336961,0.0001415261],"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":[0.0006726032,0.0002136891,0.0001266298,0.002561522,0.0002230521,0.00001378058,0.00008107074,0.00002597356,0.00002252887,0.946825,0.02543995,0.02379423],"study_design_scores_gemma":[0.0006865829,0.0006440328,0.0008132453,0.0004452244,0.0004930047,0.00001189051,0.0001337997,0.2433359,0.00004495581,0.7515281,0.001183009,0.0006803218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004661723,0.00006952693,0.9666322,0.00003466355,0.0002604331,0.0007660237,0.0006135084,0.00009967995,0.02686219],"genre_scores_gemma":[0.03363457,0.00001878852,0.9543577,0.00004240643,0.000171729,0.000125323,0.003220316,0.000099672,0.008329421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2433099,"threshold_uncertainty_score":0.9999418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2434440563427897,"score_gpt":0.4268240186958926,"score_spread":0.1833799623531029,"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."}}