{"id":"W2088270721","doi":"10.1002/asmb.876","title":"Score tests for inverse Gaussian mixtures","year":2010,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Inverse Gaussian distribution; Generalized inverse Gaussian distribution; Mixing (physics); Mathematics; Monte Carlo method; Gaussian; Applied mathematics; Inverse; Null distribution; Statistics; Goodness of fit; Mixture model; Markov chain Monte Carlo; Statistical physics; Gaussian process; Statistical hypothesis testing; Distribution (mathematics); Gaussian random field; Test statistic; Mathematical analysis; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003025336,0.000257416,0.0003858883,0.0001057499,0.000122368,0.00003793932,0.0001506455,0.000533965,0.00002632746],"category_scores_gemma":[0.0006236756,0.0002212712,0.00002685169,0.0002102289,0.0001866093,0.0001271526,0.00008090941,0.0007760665,8.18461e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001543052,"about_ca_system_score_gemma":0.00006880194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000210727,"about_ca_topic_score_gemma":0.00007727846,"domain_scores_codex":[0.9986578,0.00001185639,0.0003485961,0.0004310521,0.0001578002,0.0003929442],"domain_scores_gemma":[0.9985714,0.0007584644,0.0001070357,0.000305644,0.0001011771,0.0001562832],"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":[0.00007190803,0.00009315873,0.0000107276,0.0001357559,0.000008476615,0.000003323874,0.0001575171,0.0036085,0.002072019,0.980372,0.0002433592,0.01322328],"study_design_scores_gemma":[0.001077076,0.00001681708,0.000158956,0.00007325316,0.00002869944,0.000009627308,0.00007421,0.0594921,0.00008928233,0.938647,0.00005535592,0.0002775796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051234,0.00001369893,0.8922207,0.0001898407,0.000183563,0.0006081159,0.00004729471,0.00004430086,0.001569095],"genre_scores_gemma":[0.6691217,0.000002425223,0.3302247,0.0001277175,0.0001426199,0.0002543975,0.00000715097,0.00003702682,0.00008227002],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5639983,"threshold_uncertainty_score":0.9023179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1207386812163745,"score_gpt":0.3751467747167434,"score_spread":0.254408093500369,"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."}}