{"id":"W2605685194","doi":"10.1002/env.2445","title":"Goodness‐of‐fit tests for copula‐based spatial models","year":2017,"lang":"en","type":"article","venue":"Environmetrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Goodness of fit; Bivariate analysis; Random field; Spatial dependence; Statistics; Parametric statistics; Spatial analysis; Mathematics; Statistic; Econometrics; Multivariate statistics; Test statistic; Computer science; Statistical hypothesis testing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004047565,0.0001561236,0.0004702237,0.0003883542,0.0002687002,0.00008465113,0.0006234776,0.0001336289,0.000381695],"category_scores_gemma":[0.0006773652,0.0001771246,0.000229565,0.0001835216,0.0001107051,0.0002872104,0.00009858378,0.00008620142,0.0002261387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004529428,"about_ca_system_score_gemma":0.00001232059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112685,"about_ca_topic_score_gemma":0.000108699,"domain_scores_codex":[0.9987688,0.000007274137,0.0005150787,0.0003926413,0.00006561612,0.0002505777],"domain_scores_gemma":[0.9979866,0.0001513006,0.0007338182,0.001020351,0.000017746,0.00009014102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001194982,0.0007653309,0.8589631,0.0001956421,0.0002797138,0.000008644854,0.0001554186,0.0226451,0.0001446058,0.07132161,0.003138249,0.04226308],"study_design_scores_gemma":[0.002613615,0.0003048289,0.3825782,0.00002180071,0.0001194992,0.000001043697,0.00001946689,0.4795491,0.001390692,0.05770707,0.07479982,0.0008949191],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1576104,0.001692673,0.8251789,0.0004885147,0.0008016386,0.0005021357,0.003950122,0.00003491251,0.009740721],"genre_scores_gemma":[0.9936251,0.0001549539,0.005179798,0.00008473382,0.000143389,0.00002921695,0.0001402847,0.00002771367,0.0006147781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8360147,"threshold_uncertainty_score":0.722293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1815350213898136,"score_gpt":0.2746156126284441,"score_spread":0.0930805912386305,"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."}}