{"id":"W3007176335","doi":"10.1016/j.jspi.2020.02.001","title":"Estimation of mean squared prediction error of empirically spatial predictor of small area means under a linear mixed model","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Small area estimation; Estimator; Statistics; Mathematics; Mean squared error; Scale (ratio); Parametric statistics; Generalized linear mixed model; Econometrics; Best linear unbiased prediction; Population; Estimation; Computer science; Machine learning; Geography; Demography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002744098,0.00009696348,0.0005468797,0.0001366982,0.00002359398,0.00001122083,0.0001182695,0.00007354248,0.00006464754],"category_scores_gemma":[0.0009135138,0.00008891032,0.00006800876,0.0001370231,0.00009530107,0.0001332664,0.00002921449,0.0001551949,0.000001068437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001321008,"about_ca_system_score_gemma":0.000056333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001738846,"about_ca_topic_score_gemma":0.00001016028,"domain_scores_codex":[0.9985118,0.00002282052,0.001128379,0.0001362197,0.0001034752,0.00009728666],"domain_scores_gemma":[0.9984223,0.0002553993,0.0009661131,0.00008208819,0.0001521875,0.0001219237],"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":[0.0008537563,0.0002912334,0.1630151,0.0005253365,0.0004188773,0.000008820598,0.005036474,0.8030688,0.000761443,0.02106089,0.0004952573,0.004464088],"study_design_scores_gemma":[0.0004982675,0.0006657895,0.04595032,0.0001074511,0.00007780358,0.000002210562,0.0001220033,0.9431702,0.0001586944,0.009166051,0.00001009368,0.00007105499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2678513,0.00008877269,0.7300396,0.0001120178,0.00003676768,0.00003437483,0.001746756,0.000002951868,0.00008748723],"genre_scores_gemma":[0.9696279,0.00003730417,0.03017557,0.0000355712,0.00003633718,6.560319e-7,0.00007724196,0.000005958028,0.000003418929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7017767,"threshold_uncertainty_score":0.3625658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.122449976722164,"score_gpt":0.2876439503609797,"score_spread":0.1651939736388157,"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."}}