{"id":"W3122922071","doi":"10.1007/s42519-021-00165-4","title":"On the Estimation of Entropy for Non-negative Data","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Kernel density estimation; Entropy (arrow of time); Applied mathematics; Multivariate kernel density estimation; Statistics; Entropy estimation; Kernel (algebra); Nonparametric statistics; Invariant estimator; Kernel method; Variable kernel density estimation; Minimax estimator; Minimum-variance unbiased estimator; Artificial intelligence; Computer science; Discrete mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03065872,0.00185119,0.003391174,0.004046008,0.001064137,0.003583146,0.0036786,0.002393165,0.001562525],"category_scores_gemma":[0.1502575,0.001286465,0.001666493,0.002539643,0.009771287,0.007718124,0.007228748,0.004752912,0.0003090014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001786504,"about_ca_system_score_gemma":0.001900248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002026726,"about_ca_topic_score_gemma":0.001286261,"domain_scores_codex":[0.9753003,0.01878189,0.001046668,0.001703814,0.002694416,0.0004729958],"domain_scores_gemma":[0.7198961,0.2658689,0.003000973,0.007255961,0.002957735,0.001020341],"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":[0.0004036997,0.0001324864,0.005703453,0.0008166726,0.0005449491,0.0003793397,0.0006983489,0.2496765,0.003435847,0.5851492,0.00295124,0.1501083],"study_design_scores_gemma":[0.00002388343,0.00007926127,0.001178512,0.0001280126,0.00003927686,0.0001470786,0.00004370603,0.5871584,0.001042268,0.4092062,0.000891561,0.00006189728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007185048,0.001007047,0.9903729,0.000491453,0.00007065426,0.00003478127,0.00005413943,0.0000700269,0.0007139526],"genre_scores_gemma":[0.4638118,0.005520728,0.5225401,0.0009863194,0.002331685,0.0004746521,0.0009585621,0.0003524154,0.003023826],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03065872,"threshold_uncertainty_score":0.1621407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1693750604340235,"score_gpt":0.4795498039033348,"score_spread":0.3101747434693113,"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."}}