{"id":"W2259606755","doi":"10.1093/biostatistics/kxv054","title":"Augmented composite likelihood for copula modeling in family studies under biased sampling","year":2016,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Psoriasis Foundation","keywords":"Copula (linguistics); Statistics; Quasi-maximum likelihood; Marginal likelihood; Econometrics; Heritability; Maximum likelihood; Sampling bias; Sampling scheme; Marginal model; Importance sampling; Computer science; Mathematics; Sample size determination; Likelihood function; Regression analysis; Biology; Estimator; Genetics; Monte Carlo method","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02768387,0.001073312,0.001825214,0.001578311,0.0007555445,0.001759947,0.002797505,0.001516431,0.003489942],"category_scores_gemma":[0.1037473,0.0008673671,0.001510318,0.002361454,0.002490466,0.002235515,0.002575542,0.002918205,0.0006665584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001274375,"about_ca_system_score_gemma":0.002285075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005251951,"about_ca_topic_score_gemma":0.004370769,"domain_scores_codex":[0.9860045,0.01220462,0.0002491423,0.0006914626,0.0006787008,0.0001714878],"domain_scores_gemma":[0.8867764,0.1030371,0.003405206,0.004727918,0.001489477,0.000563875],"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":[0.0001577436,0.00006927809,0.01039182,0.0002872185,0.00038951,0.0007676131,0.0008539985,0.3678134,0.0008286118,0.5549092,0.002425444,0.0611062],"study_design_scores_gemma":[0.00001937626,0.00003611749,0.0008168896,0.00003961343,0.00003332364,0.0001248014,0.00004909885,0.7942762,0.0001707166,0.2028211,0.001588614,0.00002423764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004043172,0.0001660607,0.9952052,0.0001273974,0.00001207215,0.0000292667,0.0000631094,0.00008423896,0.0002695839],"genre_scores_gemma":[0.1950919,0.001040106,0.7989578,0.0002204653,0.0001411833,0.00102272,0.0005099202,0.0002467661,0.00276909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02768387,"threshold_uncertainty_score":0.146408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4362143197409914,"score_gpt":0.4642472793848855,"score_spread":0.02803295964389407,"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."}}