{"id":"W2138069825","doi":"10.1109/icecs.2009.5411004","title":"Robust estimation of LP parameters in white noise with unknown variance","year":2009,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Overdetermined system; Mathematics; Autoregressive model; Estimator; White noise; Noise (video); Applied mathematics; Colors of noise; Singular value decomposition; Linear least squares; Robustness (evolution); Statistics; Mathematical optimization; Algorithm; Computer science; Artificial intelligence","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":[0.0008542699,0.0006322767,0.0007770094,0.0005761737,0.0001742822,0.0006648866,0.0005808613,0.0007772597,0.0004039507],"category_scores_gemma":[0.004352797,0.0003633009,0.0004521865,0.00057406,0.0005553396,0.001127595,0.0007390523,0.0008259348,0.0003393175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002240078,"about_ca_system_score_gemma":0.0005447615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008922852,"about_ca_topic_score_gemma":0.0006967252,"domain_scores_codex":[0.9992951,0.0002184852,0.00003905972,0.0001546387,0.0002556076,0.00003708116],"domain_scores_gemma":[0.9991466,0.000432378,0.00012615,0.0001294724,0.0001515007,0.00001392861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002096168,0.00005790068,0.0008261748,0.0001555852,0.0001091138,0.0001877155,0.000119693,0.509205,0.1089487,0.0321292,0.001334317,0.3467169],"study_design_scores_gemma":[0.000008736723,0.00002139086,0.0004237395,0.000006034197,0.00001177573,0.00006337317,0.00000853748,0.9772345,0.01477996,0.00679764,0.0006264095,0.00001802046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01055951,0.00009944748,0.9888678,0.00002614667,0.000007858504,0.000004557157,0.00001568306,0.000177864,0.0002410797],"genre_scores_gemma":[0.3326612,0.0005225198,0.6644754,0.00005563209,0.00008121267,0.00006723733,0.0002432095,0.0001846865,0.001708854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0008922852,"threshold_uncertainty_score":0.004517853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184445744418661,"score_gpt":0.2421263093158744,"score_spread":0.2236817348740083,"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."}}