{"id":"W2027458694","doi":"10.1016/s0167-7152(02)00242-0","title":"Jackknifing type weighted least squares estimators in partially linear regression models","year":2002,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Science Foundation of Jiangsu Province; Hong Kong Polytechnic University","keywords":"Jackknife resampling; Mathematics; Estimator; Heteroscedasticity; Statistics; Generalized least squares; Linear regression; Linear model; Type (biology); Applied mathematics","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":[0.01881728,0.001480359,0.002378355,0.001414643,0.0007327114,0.001736911,0.004580682,0.002791698,0.002270499],"category_scores_gemma":[0.07884997,0.002115357,0.001371307,0.002359022,0.002150229,0.00433842,0.002633606,0.002688994,0.0007146213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008630595,"about_ca_system_score_gemma":0.001588928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003326257,"about_ca_topic_score_gemma":0.00393514,"domain_scores_codex":[0.9897048,0.007180163,0.0004157506,0.00114718,0.001237535,0.0003146183],"domain_scores_gemma":[0.9689456,0.02311444,0.002180575,0.00318261,0.002200645,0.0003760863],"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":[0.0003944891,0.0001667001,0.003402347,0.0005616845,0.0005485775,0.0002132873,0.0003878727,0.476408,0.002610361,0.3449347,0.003054424,0.1673175],"study_design_scores_gemma":[0.00002667752,0.00004064265,0.0004244486,0.00003202218,0.0000508774,0.00005737509,0.00002272147,0.868571,0.0006721426,0.1286353,0.00143797,0.00002873345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002319625,0.00009476837,0.9973243,0.00003644619,0.0000154883,0.00001175338,0.00001406658,0.00006005661,0.0001234068],"genre_scores_gemma":[0.1545797,0.0006869918,0.83711,0.0001884288,0.0001707079,0.0004143664,0.0005273142,0.0003617003,0.005960851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01881728,"threshold_uncertainty_score":0.09951645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1477677850914575,"score_gpt":0.383524352070981,"score_spread":0.2357565669795235,"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."}}