{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03387259,0.001627635,0.002852572,0.006395862,0.001675152,0.003743644,0.004137955,0.003404156,0.009837131],"category_scores_gemma":[0.2491997,0.0006636505,0.003088454,0.00462412,0.00971132,0.007048184,0.006415118,0.00411063,0.001291188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421387,"about_ca_system_score_gemma":0.002010216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001193711,"about_ca_topic_score_gemma":0.0007278701,"domain_scores_codex":[0.9651954,0.02208909,0.00150573,0.003279947,0.006764092,0.001165834],"domain_scores_gemma":[0.7642471,0.2002486,0.01095399,0.0125152,0.008736547,0.003298661],"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":[0.001928017,0.0003173623,0.0429578,0.0004618016,0.001151786,0.0005182276,0.001036128,0.07867449,0.002556694,0.6578128,0.005256963,0.2073279],"study_design_scores_gemma":[0.0003368253,0.0008603982,0.0143734,0.0001127245,0.0001947162,0.0004587436,0.0004095168,0.3422143,0.002863271,0.6338177,0.004147562,0.0002109019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1430507,0.0004868611,0.8476313,0.0006693252,0.0001542119,0.0003648888,0.0006490906,0.0005719653,0.006421571],"genre_scores_gemma":[0.7847568,0.0002519185,0.2090186,0.0003069665,0.0002659977,0.0008581544,0.001577307,0.0002163157,0.002747901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03387259,"threshold_uncertainty_score":0.1791375,"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."}}