{"id":"W1850438056","doi":"","title":"A pseudo-GEE approach to analyzing longitudinal surveys under imputation por missing responses","year":2011,"lang":"en","type":"article","venue":"Journal of Official Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Missing data; Estimator; Statistics; Imputation (statistics); Mathematics; Generalized estimating equation; Gee; Marginal model; Econometrics; Estimating equations; Regression analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.003141922,0.0002172281,0.0005507143,0.0002874408,0.0001600872,0.000100513,0.0002504395,0.00009912103,0.0001256531],"category_scores_gemma":[0.007237764,0.0001840981,0.00009857342,0.0003667466,0.00009141378,0.0001233077,0.00004943422,0.0003298846,0.000008768439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107241,"about_ca_system_score_gemma":0.0002939611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005804031,"about_ca_topic_score_gemma":0.0000239915,"domain_scores_codex":[0.9970577,0.0008287164,0.001043286,0.0002061748,0.000523523,0.0003405467],"domain_scores_gemma":[0.9955702,0.002538553,0.0007171099,0.0001715395,0.0007090147,0.0002935525],"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":[0.0008431117,0.0009223334,0.004016563,0.0001847235,0.0002464467,0.0002736952,0.002319008,0.00006519664,0.0005110805,0.8151768,0.003367151,0.1720739],"study_design_scores_gemma":[0.0004976595,0.0006740488,0.1054387,0.0001317469,0.0002782935,0.0002217388,0.0003601934,0.002692313,0.0003273406,0.889004,0.00003464257,0.0003392851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01343556,0.00002338272,0.9848474,0.000053938,0.0003099182,0.0001345961,0.0001424223,0.00001816021,0.001034623],"genre_scores_gemma":[0.2122312,0.0000051564,0.7874433,0.00004487539,0.0002101887,0.000001288566,0.000002753187,0.00002762252,0.000033583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1987957,"threshold_uncertainty_score":0.8664802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1917411910339478,"score_gpt":0.3977617229681855,"score_spread":0.2060205319342377,"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."}}