{"id":"W2022824681","doi":"10.1016/j.jmva.2014.12.006","title":"Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts","year":2014,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Estimator; Covariance; Minimum-variance unbiased estimator; Statistics; Upper and lower bounds; Efficiency; Econometrics; Conditional expectation; Efficient estimator; Conditional variance; Semiparametric model; Applied mathematics; Autoregressive conditional heteroskedasticity","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005112469,0.000232562,0.001359692,0.002768802,0.00009601909,0.0001447069,0.0008350694,0.00009064891,0.000195788],"category_scores_gemma":[0.009741333,0.0001548942,0.0003458889,0.00957277,0.00008626501,0.0002620562,0.0001626353,0.000450467,0.000003518936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000875311,"about_ca_system_score_gemma":0.00007486613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003707002,"about_ca_topic_score_gemma":0.00032697,"domain_scores_codex":[0.9966244,0.0006507135,0.001179319,0.0004180973,0.0007793062,0.0003481941],"domain_scores_gemma":[0.9930294,0.004344878,0.001132257,0.0009283106,0.0003781353,0.0001869881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002109568,0.0009701297,0.9453868,0.00006973177,0.01415173,0.0001217789,0.000509145,0.01420004,0.0002999374,0.01169392,0.00005414416,0.01233173],"study_design_scores_gemma":[0.0009499121,0.00026358,0.5042996,0.0000719127,0.02253442,0.00001549365,0.0001328341,0.4643767,0.00003408403,0.006969732,0.00004865866,0.0003030148],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3709333,0.00004680107,0.6286163,0.00006454303,0.00002739441,0.00004037577,0.00001120074,0.00000649824,0.0002536253],"genre_scores_gemma":[0.6950814,0.0000146017,0.3047884,0.00001658535,0.00005310329,7.598114e-7,0.000005900447,0.00001039684,0.00002894784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4501767,"threshold_uncertainty_score":0.9986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1033551850154679,"score_gpt":0.4048633497959919,"score_spread":0.301508164780524,"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."}}