{"id":"W1545490169","doi":"10.2139/ssrn.1333779","title":"Nonstandard Estimation of Inverse Conditional Density-Weighted Expectations","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Inverse; Mathematics; Estimation; Econometrics; Statistics; Conditional expectation; Density estimation; Conditional variance; Applied mathematics; Economics; Estimator; Autoregressive conditional heteroskedasticity","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":[0.0007241733,0.00009691609,0.0001898732,0.0001015489,0.0001353653,0.00001903335,0.0001005361,0.00005155827,0.0001137691],"category_scores_gemma":[0.000783987,0.00008424483,0.00007197978,0.0001525529,0.0000614891,0.0001034042,0.000007079799,0.0005905217,0.0000107028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002686871,"about_ca_system_score_gemma":0.0008589429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003058731,"about_ca_topic_score_gemma":0.00002143733,"domain_scores_codex":[0.9985441,0.0001093405,0.000344597,0.00009905363,0.0002871394,0.0006157598],"domain_scores_gemma":[0.9990138,0.0003982286,0.0002066805,0.00009996744,0.000217073,0.0000642689],"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":[0.00002756881,0.00007340139,0.00003863949,0.000003418088,0.00003532547,0.000001840504,0.0001473942,0.00001453069,0.0003659652,0.9725434,0.0002921541,0.02645636],"study_design_scores_gemma":[0.0004289396,0.0003180876,0.0003776425,0.00002648577,0.00004623828,0.0001558498,0.0005989121,0.004094556,0.0008670513,0.9929812,0.00001477383,0.00009026861],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1244704,0.00005793124,0.8736744,0.0003895175,0.00004665381,0.00007414805,0.00001327613,0.00002022903,0.001253489],"genre_scores_gemma":[0.8224451,0.00004725362,0.1773055,0.00004590131,0.0000630438,0.000001538382,0.00000689125,0.000006132182,0.00007871001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6979747,"threshold_uncertainty_score":0.3435404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677107602376161,"score_gpt":0.3426619230159393,"score_spread":0.3158908469921777,"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."}}