{"id":"W2049916971","doi":"10.1002/cjs.5550340106","title":"Empirical likelihood tests for two-sample problems via nonparametric density estimation","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad","keywords":"Mathematics; Empirical likelihood; Estimator; Statistics; Test statistic; Nonparametric statistics; Kernel density estimation; Statistical hypothesis testing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006331964,0.0001257023,0.0002342215,0.0003487158,0.0001660525,0.0001874787,0.000389922,0.00006371919,0.000006229123],"category_scores_gemma":[0.0006593418,0.0001171659,0.00006238738,0.0004305823,0.00004820282,0.0002220842,0.00001396942,0.0001766043,0.00000341845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001665002,"about_ca_system_score_gemma":0.001119507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003610794,"about_ca_topic_score_gemma":0.01368057,"domain_scores_codex":[0.9987563,0.00006897844,0.0004418823,0.0001634812,0.0001938884,0.0003754802],"domain_scores_gemma":[0.997821,0.0006731711,0.00028305,0.0001991772,0.0005451336,0.0004784302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001125913,0.0001013327,0.008404493,0.0001039254,0.0000520092,0.0002552958,0.0005506405,0.006145352,0.0001883887,0.1897874,0.0648245,0.7295754],"study_design_scores_gemma":[0.0004672805,0.0002083865,0.006022038,0.00002506055,0.00003099743,0.000191463,0.000001559402,0.3184083,0.0001519668,0.6723367,0.001986693,0.0001695418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001267728,0.0001503069,0.9973222,0.0004694648,0.0004243982,0.000163303,0.000119201,0.000009201909,0.00007421851],"genre_scores_gemma":[0.1942338,0.000001440104,0.8053817,0.0002098201,0.000132473,0.000002373233,0.00000886309,0.000009728826,0.00001984688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7294058,"threshold_uncertainty_score":0.763408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02904746549342792,"score_gpt":0.2929953656221195,"score_spread":0.2639479001286916,"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."}}