{"id":"W2978725510","doi":"","title":"UMP invariance in adaptive detection: kernels that preserve monotone likelihood ratio","year":2003,"lang":"en","type":"article","venue":"IEEE Signal Processing Workshop on Statistical Signal Processing","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Monotone polygon; Invariant (physics); Statistic; Estimator; Sufficient statistic; Test statistic; Applied mathematics; Noise power; Likelihood-ratio test; Statistics; Statistical hypothesis testing; Power (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":[0.007120606,0.000651674,0.001013725,0.001011803,0.0004516898,0.001541754,0.001235859,0.0008612383,0.001164878],"category_scores_gemma":[0.03817611,0.0003948144,0.0007366317,0.0007715064,0.003642782,0.003646829,0.00269964,0.001513364,0.0002495797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008534233,"about_ca_system_score_gemma":0.0008526065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008724195,"about_ca_topic_score_gemma":0.0004120085,"domain_scores_codex":[0.9969485,0.001269205,0.0001507354,0.0004529741,0.0008282537,0.0003504202],"domain_scores_gemma":[0.9829856,0.01245576,0.001467913,0.001269203,0.00130223,0.000519291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003857515,0.0000985213,0.005638642,0.0001627554,0.00009232933,0.0005012358,0.0003104221,0.07631877,0.02523655,0.818859,0.0008610994,0.07153484],"study_design_scores_gemma":[0.00005143473,0.0002935985,0.00242309,0.00002386696,0.00002670084,0.0003195951,0.00008448325,0.6465861,0.008950134,0.340433,0.0007675444,0.00004051104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07286013,0.0001431376,0.9244409,0.000314806,0.00001583832,0.00002815497,0.00004014131,0.00009681618,0.002059988],"genre_scores_gemma":[0.8632235,0.0002724146,0.1346777,0.0002151904,0.0001242167,0.0000957391,0.0001215875,0.0001235051,0.001146301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007120606,"threshold_uncertainty_score":0.03765786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118632328080612,"score_gpt":0.2591838037555631,"score_spread":0.227997480474757,"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."}}