{"id":"W2167199711","doi":"10.19026/rjaset.5.4964","title":"Empirical Bayes Estimation for Exponential Model Using Non-parameter Polynomial Density Estimator","year":2013,"lang":"en","type":"article","venue":"Research Journal of Applied Sciences Engineering and Technology","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Natural Science Foundation of Jiangxi Province","keywords":"Estimator; Mean squared error; Mathematics; Bayes' theorem; Statistics; Bayes estimator; Probability density function; Applied mathematics; Exponential function; Estimation theory; Exponential distribution; Efficient estimator; Bayes error rate; Minimum-variance unbiased estimator; Bayesian probability; Bayes classifier; Mathematical analysis","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":[0.0008271137,0.0000855015,0.000189824,0.0004461518,0.000284818,0.00008894908,0.000203761,0.0001048095,0.00001032494],"category_scores_gemma":[0.001407534,0.00007112762,0.00002981676,0.0004827703,0.0003679505,0.0001203161,0.00006173189,0.0002581807,0.000003368518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000553455,"about_ca_system_score_gemma":0.000118297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001845831,"about_ca_topic_score_gemma":1.754992e-7,"domain_scores_codex":[0.9989261,0.000008956914,0.0003193577,0.0001538184,0.0003067002,0.000285087],"domain_scores_gemma":[0.9986464,0.0007241094,0.0000990748,0.00009631213,0.0003248159,0.0001092485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004444156,0.0002097534,0.00008844413,0.0001676136,0.00005113118,0.000002405157,0.0001603048,0.08137929,0.1762055,0.7253828,0.004871728,0.01143653],"study_design_scores_gemma":[0.0002073699,0.00005984191,0.00005327258,0.00001866698,0.000009114656,0.00002742328,0.00006573311,0.8247387,0.007907553,0.1668355,0.00001638601,0.00006047797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3916555,0.000006657433,0.6076201,0.0004743194,0.00001732224,0.0001789356,0.000004298606,0.00001832694,0.00002453421],"genre_scores_gemma":[0.608909,0.000001522284,0.3910317,0.000004093457,0.00001800625,0.00002777192,6.290227e-7,0.000004647846,0.000002718614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7433594,"threshold_uncertainty_score":0.29005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1373203094670877,"score_gpt":0.4303016660535384,"score_spread":0.2929813565864506,"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."}}