{"id":"W2056519252","doi":"10.1111/j.1541-0420.2008.01105.x","title":"Median Regression Models for Longitudinal Data with Dropouts","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Statistics; Estimator; Regression; Dropout (neural networks); Regression analysis; Consistency (knowledge bases); Regression diagnostic; Mathematics; Regression toward the mean; Linear regression; Longitudinal data; Computer science; Polynomial regression; Data mining; Machine learning","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":[0.0004410306,0.0001418485,0.0002572654,0.0004302542,0.0001477718,0.00002547821,0.0004318543,0.0000865098,0.00003551975],"category_scores_gemma":[0.003275692,0.00009143026,0.00002707255,0.001585433,0.0001264542,0.0001617375,0.0001297149,0.0000845118,0.000006777278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002838452,"about_ca_system_score_gemma":0.00007912888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001339238,"about_ca_topic_score_gemma":0.000004592679,"domain_scores_codex":[0.9987286,0.00003623014,0.000228221,0.0003653658,0.0003803198,0.0002613171],"domain_scores_gemma":[0.9968179,0.002040133,0.0001150729,0.0007188052,0.0001653926,0.0001426232],"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":[0.0002591261,0.000657975,0.006518051,0.0005185531,0.0001212502,0.0002012004,0.0003371549,0.000001296793,0.0001863058,0.6271976,0.06604654,0.2979549],"study_design_scores_gemma":[0.001143853,0.0004850059,0.001858326,0.0001372424,0.000104579,0.0001111779,0.00004361206,0.05224606,0.0005005221,0.9398539,0.003066828,0.0004488757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001724803,0.0001058061,0.9964874,0.0001502838,0.0001305758,0.0002406997,0.00035455,0.000056972,0.000748919],"genre_scores_gemma":[0.08153992,0.00006875968,0.9180302,0.00002519756,0.0001066911,0.00001440593,0.00005488003,0.00002309073,0.0001368567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3126563,"threshold_uncertainty_score":0.3921547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.385891752270819,"score_gpt":0.4393660521375881,"score_spread":0.0534742998667691,"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."}}