{"id":"W4383682323","doi":"10.3329/jsr.v56i2.67468","title":"Approximate methods for analyzing semiparametric longitudinal models with nonignorable missing responses","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Qassim University","keywords":"Missing data; Statistical inference; Inference; Econometrics; Semiparametric regression; Longitudinal data; Computer science; Variance (accounting); Statistics; Monte Carlo method; Regression; Mathematics; Data mining; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02078058,0.001360513,0.001947276,0.002944058,0.000693427,0.001550707,0.003948193,0.00178051,0.003558278],"category_scores_gemma":[0.08164223,0.001515363,0.002228283,0.002966193,0.002478941,0.00335015,0.003257895,0.003653082,0.0008187193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423047,"about_ca_system_score_gemma":0.002181511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00260683,"about_ca_topic_score_gemma":0.00281777,"domain_scores_codex":[0.9882886,0.00925787,0.0003674135,0.0006599554,0.001255547,0.0001705259],"domain_scores_gemma":[0.9330147,0.05674208,0.003667852,0.004463919,0.001731535,0.0003799389],"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":[0.0001066954,0.0001034288,0.003434265,0.000631927,0.0005919132,0.0001615872,0.000367576,0.3596539,0.0008139731,0.5252728,0.002645361,0.1062165],"study_design_scores_gemma":[0.00002888681,0.00005715184,0.0004021537,0.00008057987,0.00004912997,0.00007729782,0.00004069236,0.6948925,0.0002704029,0.3011222,0.00295051,0.00002843436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006197602,0.00024644,0.9987875,0.00007577417,0.00001262021,0.0000175086,0.00003140535,0.00005799316,0.0001510758],"genre_scores_gemma":[0.09946416,0.002223362,0.8934032,0.0002908925,0.0003015721,0.001344744,0.0006271818,0.0001897332,0.00215508],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02078058,"threshold_uncertainty_score":0.1098995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4642544285858737,"score_gpt":0.592966117563545,"score_spread":0.1287116889776713,"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."}}