{"id":"W2022019263","doi":"10.1002/cjs.5550340205","title":"Robust inference in generalized linear models for longitudinal data","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Outlier; Weighting; Inference; Generalized linear model; Computer science; Maximum likelihood; Longitudinal data; Linear model; Statistics; Econometrics; Mathematics; Data mining; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.04151838,0.001318938,0.002622026,0.002434766,0.0006706329,0.002222483,0.003362901,0.002355196,0.002677026],"category_scores_gemma":[0.1721887,0.001293975,0.002087598,0.00298143,0.002824959,0.002102873,0.003093732,0.003322788,0.0004673919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002122938,"about_ca_system_score_gemma":0.003028539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067207,"about_ca_topic_score_gemma":0.006893714,"domain_scores_codex":[0.9610951,0.03411858,0.0007382958,0.001634648,0.001937351,0.0004760214],"domain_scores_gemma":[0.8615679,0.1272875,0.004449277,0.003803679,0.002489984,0.0004016799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001997048,0.00007652324,0.002072121,0.0004982697,0.001117913,0.0002394289,0.0003053041,0.585905,0.0006871547,0.3412771,0.003587756,0.06403374],"study_design_scores_gemma":[0.00005949184,0.00004296019,0.0003609025,0.00005211411,0.00005416571,0.00003324224,0.00002525383,0.7650672,0.0002386,0.2326438,0.001395639,0.00002655339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002187888,0.0005613095,0.9963613,0.0003766402,0.00004308132,0.0000208498,0.00006931643,0.0001622423,0.0002173902],"genre_scores_gemma":[0.2452049,0.002191996,0.7478114,0.0004800375,0.0004127942,0.0005915964,0.0007724207,0.0003543207,0.00218041],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04151838,"threshold_uncertainty_score":0.2195728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4386275487445401,"score_gpt":0.4202562033916826,"score_spread":0.01837134535285756,"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."}}