{"id":"W3091993350","doi":"10.1002/cjs.11572","title":"Efficient nonparametric estimation for skewed distributions","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; University of Ottawa","funders":"","keywords":"Estimator; Mean squared error; Mathematics; Statistics; Bias of an estimator; Efficient estimator; Efficiency; Nonparametric statistics; Consistent estimator; Context (archaeology); Econometrics; Minimum-variance unbiased estimator; Conditional expectation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01388639,0.0005486677,0.001538586,0.002023719,0.0004636525,0.001562584,0.001574102,0.0010914,0.001692037],"category_scores_gemma":[0.07914227,0.0005048474,0.0006870443,0.001875821,0.001454307,0.001637751,0.002073968,0.001937418,0.0004043205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008319815,"about_ca_system_score_gemma":0.001143183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919499,"about_ca_topic_score_gemma":0.00167118,"domain_scores_codex":[0.993134,0.004251538,0.0002738059,0.0007318766,0.001320569,0.0002881918],"domain_scores_gemma":[0.9449687,0.04468182,0.003351956,0.003763368,0.002984701,0.0002494223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002398398,0.0001295209,0.0150442,0.0003732556,0.0003772299,0.0003885764,0.000258516,0.5803842,0.004131401,0.1417149,0.003125357,0.253833],"study_design_scores_gemma":[0.000017389,0.00003310175,0.003112963,0.00005487707,0.00001768117,0.00009746567,0.00003566447,0.9272372,0.001070854,0.06715808,0.0011439,0.00002082672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01381677,0.0002145729,0.9852293,0.00008948764,0.0000147206,0.00003019582,0.00005509532,0.0001124929,0.0004372839],"genre_scores_gemma":[0.6170406,0.0006204863,0.3791849,0.000209235,0.0001404655,0.0003260572,0.0006234896,0.0001321099,0.001722704],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01388639,"threshold_uncertainty_score":0.07343912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1169882899278286,"score_gpt":0.3822802208548926,"score_spread":0.265291930927064,"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."}}