{"id":"W1984579286","doi":"10.1080/02664763.2012.711814","title":"Spatial modeling using frequentist approach for disease mapping","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Frequentist inference; Generalized linear mixed model; Markov chain Monte Carlo; Bayesian probability; Computer science; Econometrics; Spatial econometrics; Statistics; Data mining; Bayesian inference; Machine learning; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005641708,0.0007303092,0.001641036,0.00411279,0.001061293,0.001746305,0.00221304,0.001580524,0.002156511],"category_scores_gemma":[0.01697487,0.000747625,0.002431861,0.003755073,0.0009755163,0.001831264,0.001799738,0.001595024,0.0003491384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440597,"about_ca_system_score_gemma":0.001196553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01533646,"about_ca_topic_score_gemma":0.01187956,"domain_scores_codex":[0.9955402,0.002897681,0.0001580211,0.0008701263,0.0003802007,0.0001536978],"domain_scores_gemma":[0.9897252,0.008050358,0.0008579356,0.0008157845,0.0004107894,0.0001398454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008268096,0.00008871396,0.01764358,0.000193049,0.0006329688,0.00040896,0.0005125111,0.7940243,0.0005718299,0.1042293,0.001883845,0.07972819],"study_design_scores_gemma":[0.000008038771,0.0000196696,0.001048434,0.00001272028,0.0000316332,0.00008974917,0.00005567059,0.9467008,0.00009216861,0.05070633,0.001219349,0.00001539089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01225221,0.000286086,0.986233,0.0002734889,0.00002877775,0.00004243426,0.000258884,0.0001709262,0.0004542797],"genre_scores_gemma":[0.4996712,0.0008860151,0.4951305,0.0001873828,0.0001735506,0.0005342721,0.001165018,0.00008224736,0.002169735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01533646,"threshold_uncertainty_score":0.03049439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105978344911372,"score_gpt":0.2590945527456843,"score_spread":0.148496718254547,"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."}}