{"id":"W2419445839","doi":"10.5539/ijsp.v5n4p9","title":"Gradient and Likelihood Ratio Tests in Cure Rate Models","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Likelihood-ratio test; Weibull distribution; Mathematics; Statistics; Statistic; Population; Sample size determination; Applied mathematics; Statistical hypothesis testing; Likelihood function; Score test; Sample (material); Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02672441,0.001624322,0.002600426,0.004375981,0.0007151036,0.002432169,0.002943051,0.003372528,0.004413242],"category_scores_gemma":[0.1294754,0.0007746315,0.002104005,0.002999549,0.004946784,0.005283649,0.002638977,0.003232173,0.001188467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001210952,"about_ca_system_score_gemma":0.001433154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002630545,"about_ca_topic_score_gemma":0.001105162,"domain_scores_codex":[0.9780812,0.01779917,0.000416654,0.001722231,0.001505267,0.0004755909],"domain_scores_gemma":[0.9144318,0.07683569,0.003594597,0.002778707,0.001637908,0.0007214057],"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":[0.001138811,0.0002227046,0.01886086,0.0007412514,0.0005945487,0.001374469,0.0006232312,0.2921944,0.001672248,0.5083086,0.004675422,0.1695933],"study_design_scores_gemma":[0.0001388041,0.0004157927,0.004207531,0.0001039276,0.000137347,0.0008611268,0.0001249129,0.6392505,0.001576365,0.3485596,0.004461896,0.000162228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03759175,0.002142583,0.9550465,0.0007912065,0.0001035318,0.0002019715,0.0003313739,0.0006833781,0.003107667],"genre_scores_gemma":[0.6596744,0.00193778,0.3308602,0.0005872583,0.0003782875,0.0008348665,0.00102947,0.0003416459,0.004356093],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02672441,"threshold_uncertainty_score":0.1413339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01963797537727257,"score_gpt":0.2815171617360204,"score_spread":0.2618791863587479,"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."}}