{"id":"W4283207738","doi":"10.1002/cjs.11706","title":"Distributed estimation with empirical likelihood","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Computer science; Divide and conquer algorithms; Consistency (knowledge bases); Asymptotic distribution; Normality; Empirical likelihood; Sample (material); Empirical research; Estimation; Statistics; Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009725798,0.001309397,0.001976732,0.002041331,0.0005818967,0.002372527,0.003080494,0.001926951,0.003379104],"category_scores_gemma":[0.05475513,0.0009829313,0.001294232,0.002378686,0.003732949,0.003866956,0.003321604,0.003848177,0.0007984377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686644,"about_ca_system_score_gemma":0.001848996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00321063,"about_ca_topic_score_gemma":0.001942774,"domain_scores_codex":[0.992794,0.004432886,0.0002540759,0.001130935,0.001085202,0.0003029593],"domain_scores_gemma":[0.9621817,0.03066116,0.001952001,0.002678358,0.00216743,0.0003594609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001673042,0.00007868727,0.002586893,0.0002701448,0.000167049,0.0002569244,0.000164869,0.6115503,0.001135503,0.3021295,0.002793014,0.07869983],"study_design_scores_gemma":[0.00002236948,0.00001914235,0.0002388397,0.0000230705,0.00001101793,0.00004781402,0.00001809193,0.9155958,0.0003313752,0.08287071,0.0008067137,0.00001506943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002055151,0.0002633725,0.996923,0.0001990826,0.00002293301,0.00002192503,0.00002644052,0.00008233768,0.0004057252],"genre_scores_gemma":[0.3967946,0.001502303,0.5943703,0.0005234717,0.0005874985,0.0004406727,0.000571322,0.0002355508,0.004974309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009725798,"threshold_uncertainty_score":0.05143553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08054540798775184,"score_gpt":0.3362861210163824,"score_spread":0.2557407130286306,"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."}}