{"id":"W2964233174","doi":"10.1111/insr.12293","title":"Small Area Quantile Estimation","year":2018,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Project 211; Program of Shanghai Subject Chief Scientist; Yunnan University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Small area estimation; Quantile; Pooling; Statistics; Estimator; Computer science; Sample size determination; Resampling; Sample (material); Econometrics; Sampling (signal processing); Contrast (vision); Population; Mean squared error; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005306715,0.0001482824,0.0003025121,0.00003786094,0.00006428124,0.00005020544,0.0002793437,0.0000436036,0.009533396],"category_scores_gemma":[0.01415181,0.0001178534,0.0000549888,0.0001175273,0.00017528,0.0000607575,0.00006550538,0.0001250382,0.0006823404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005146136,"about_ca_system_score_gemma":0.00004039204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000163704,"about_ca_topic_score_gemma":0.00001118714,"domain_scores_codex":[0.9985407,0.0001161582,0.0005102619,0.0002666322,0.0003640901,0.0002021517],"domain_scores_gemma":[0.9969122,0.002288084,0.0001348373,0.0002119324,0.0003285169,0.0001243862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006772716,0.00004402401,0.00003072718,0.0002437514,0.00001706393,0.000008776804,0.000006759637,1.773522e-8,0.000008678586,0.726402,0.01746393,0.2557675],"study_design_scores_gemma":[0.000121364,0.00008836026,0.0006307102,0.001541891,0.00006006427,0.00002504871,0.000002017742,0.007987364,0.00004949653,0.9527016,0.03663143,0.0001606932],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004325798,0.0004700324,0.9716302,0.001108367,0.0003986529,0.0002274807,0.0001929151,0.00006443504,0.02586466],"genre_scores_gemma":[0.007800358,0.001014828,0.9893839,0.001284174,0.0001770017,0.00004461287,0.00004775483,0.00001739878,0.0002299994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2556068,"threshold_uncertainty_score":0.9941524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1847528740441207,"score_gpt":0.4619305233138399,"score_spread":0.2771776492697192,"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."}}