{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04657931,0.001307554,0.002898646,0.002177356,0.0008726286,0.00229381,0.004879086,0.002828136,0.007961573],"category_scores_gemma":[0.1094265,0.0009815969,0.002490772,0.003404926,0.002111653,0.004959077,0.003238742,0.004535537,0.001534385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00173601,"about_ca_system_score_gemma":0.00188059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005474594,"about_ca_topic_score_gemma":0.00428076,"domain_scores_codex":[0.9830025,0.01247099,0.0006711893,0.001810407,0.001425937,0.0006190031],"domain_scores_gemma":[0.9251786,0.06233569,0.005430613,0.003444506,0.003081419,0.0005291948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004455722,0.0001996307,0.0153492,0.0006109152,0.0008697786,0.000608532,0.0009684291,0.4099598,0.0006669342,0.4526376,0.005931793,0.1117518],"study_design_scores_gemma":[0.00006552635,0.00009934443,0.001684129,0.000110594,0.0001081072,0.0001006239,0.00008863094,0.7421393,0.0002488375,0.251428,0.003881758,0.00004523093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01358921,0.001024581,0.9827827,0.0008911049,0.00009357514,0.00009103791,0.0003890336,0.0002747406,0.0008639426],"genre_scores_gemma":[0.5408614,0.004129472,0.4341271,0.0008368014,0.0006641209,0.002409941,0.002860211,0.0003341383,0.01377686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04657931,"threshold_uncertainty_score":0.2463379,"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."}}