{"id":"W2002475204","doi":"10.1002/cjs.10118","title":"Semiparametric transformation models for multivariate panel count data with dependent observation process","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Multivariate statistics; Covariate; Semiparametric regression; Inference; Event (particle physics); Econometrics; Computer science; Estimating equations; Semiparametric model; Statistical inference; Statistics; Count data; Regression analysis; Transformation (genetics); Regression; Mathematics; Artificial intelligence; Estimator; Nonparametric statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007987436,0.0001348778,0.000269343,0.0001822367,0.0001081262,0.00005538551,0.0003772197,0.0000659267,0.00006184407],"category_scores_gemma":[0.002121184,0.0001059826,0.00001801072,0.0001896759,0.0000657768,0.0003957926,0.000005618812,0.0001719936,0.000001099564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021689,"about_ca_system_score_gemma":0.001063719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001557968,"about_ca_topic_score_gemma":0.005388564,"domain_scores_codex":[0.9987095,0.00005178332,0.0005782642,0.0001337888,0.0002613581,0.0002652812],"domain_scores_gemma":[0.9972231,0.000788639,0.0004196074,0.0002363142,0.0009713868,0.0003608876],"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":[0.0002289543,0.0001026858,0.0008731184,0.0006501636,0.0001693523,0.00007949151,0.008330716,0.0005763457,0.00001584401,0.9353566,0.001820373,0.05179632],"study_design_scores_gemma":[0.0009195639,0.0004129571,0.001995123,0.000150299,0.0002468326,0.00006969141,0.0006436656,0.1754896,0.0001223996,0.8195409,0.0001936389,0.0002153284],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00603309,0.00002997792,0.9897277,0.00003853778,0.0001392625,0.0003061071,0.003333773,0.00000542512,0.0003860675],"genre_scores_gemma":[0.3285576,0.000008326268,0.6712722,0.00004547148,0.00003069579,0.000006160275,0.00004530661,0.0000174428,0.00001676096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3225245,"threshold_uncertainty_score":0.4321845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4555926475713832,"score_gpt":0.3650784029503741,"score_spread":0.0905142446210091,"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."}}