{"id":"W2146051901","doi":"","title":"Adapting Kernel Estimation to Uncertain Smoothness","year":2011,"lang":"en","type":"article","venue":"London School of Economics and Political Science Research Online (London School of Economics and Political Science)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Estimator; Mathematics; Smoothness; Bandwidth (computing); Kernel (algebra); Mean squared error; Rate of convergence; Kernel density estimation; Statistics; Kernel method; Kernel smoother; Applied mathematics; Mathematical optimization; Computer science; Mathematical analysis; Combinatorics; Radial basis function kernel","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","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.01016798,0.0003757545,0.0009343291,0.0009753596,0.0005537578,0.0003945442,0.001345506,0.00019594,0.0002868921],"category_scores_gemma":[0.02016171,0.0003317193,0.0001187493,0.0009770177,0.006838247,0.000937686,0.001097827,0.0006673248,0.00003286346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00055612,"about_ca_system_score_gemma":0.002723425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790435,"about_ca_topic_score_gemma":0.0002179174,"domain_scores_codex":[0.9933965,0.0002156409,0.001556196,0.001218052,0.0004797024,0.00313394],"domain_scores_gemma":[0.9895818,0.002917879,0.0002886025,0.0007661888,0.0007943398,0.00565115],"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":[0.00008271692,0.0001915251,0.004476557,0.00007699429,0.00001159767,0.000001931322,0.00008089598,0.00002831516,0.0005592299,0.990507,0.00002593877,0.00395729],"study_design_scores_gemma":[0.0006455457,0.0008122129,0.04639814,0.0001330026,0.00002637203,0.00002336855,0.0007969013,0.05376997,0.006011647,0.8906769,0.0002464785,0.0004595342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821462,0.00004835746,0.001695395,0.00222311,0.0001787015,0.0005498854,0.000293568,0.00001808714,0.01284668],"genre_scores_gemma":[0.8700342,0.0001983483,0.1291469,0.0003438345,0.0001551264,0.00001923705,0.000003169698,0.0000247483,0.000074444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1274515,"threshold_uncertainty_score":0.9999135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1986966583764333,"score_gpt":0.4395360449299459,"score_spread":0.2408393865535126,"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."}}