{"id":"W2080246954","doi":"10.1006/jmva.2000.1954","title":"The Law of the Iterated Logarithm and Central Limit Theorem for L-Statistics","year":2001,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina; Lakehead University","funders":"","keywords":"Law of the iterated logarithm; Mathematics; Iterated logarithm; Central limit theorem; Logarithm; Order statistic; Statistics; Rate of convergence; Applied mathematics; Mathematical analysis; Key (lock)","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":[0.003647756,0.0001004896,0.0004150875,0.0001329988,0.0003760608,0.0002098536,0.0006775789,0.00006441554,0.00003470416],"category_scores_gemma":[0.002796951,0.00003983968,0.0004327868,0.0009552753,0.0003099102,0.0001898111,0.00008516809,0.0001695134,7.904929e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002565086,"about_ca_system_score_gemma":0.00007003699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001457304,"about_ca_topic_score_gemma":0.0005357372,"domain_scores_codex":[0.9974643,0.0004437285,0.00100274,0.0001562701,0.0007269533,0.000206033],"domain_scores_gemma":[0.9944196,0.002998941,0.001004125,0.0003753034,0.001113806,0.00008818728],"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":[0.002913702,0.0009997707,0.07643974,0.00002543338,0.008923699,0.00004107133,0.01309323,0.1275756,0.003281666,0.6309621,0.002966522,0.1327774],"study_design_scores_gemma":[0.001379154,0.0001744297,0.1069749,0.0000229848,0.001784516,0.00003353545,0.001149096,0.4713282,0.000618878,0.4037898,0.01258937,0.0001550899],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3841073,0.0005070117,0.6097258,0.004503831,0.0003639112,0.0002333281,0.0001280842,0.000004207784,0.00042652],"genre_scores_gemma":[0.9928623,0.0001780606,0.006397014,0.0001049314,0.0000581776,9.565422e-7,8.393141e-7,0.000004206161,0.0003935591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6087549,"threshold_uncertainty_score":0.3348413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06790494807289907,"score_gpt":0.3634088338463151,"score_spread":0.295503885773416,"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."}}