{"id":"W2963237203","doi":"10.5539/ijsp.v8n1p135","title":"Empirical Likelihood Inference for Partial Functional Linear Regression Models Based on B-spline","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Empirical likelihood; Mathematics; Statistics; Linear regression; Inference; Regression analysis; Likelihood principle; Regression; Applied mathematics; Econometrics; Likelihood function; Confidence interval; Maximum likelihood; Computer science; Quasi-maximum likelihood; 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":[0.01113119,0.001316665,0.002289591,0.002327261,0.0006778183,0.00188258,0.002665948,0.002022402,0.003301974],"category_scores_gemma":[0.06888904,0.001052712,0.002022931,0.001974896,0.002338812,0.003482818,0.002990872,0.003782952,0.0007539539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008650292,"about_ca_system_score_gemma":0.001644063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005179106,"about_ca_topic_score_gemma":0.002990132,"domain_scores_codex":[0.9945642,0.003607442,0.0001777943,0.0006758618,0.0007896894,0.0001849493],"domain_scores_gemma":[0.9592469,0.03510677,0.001721769,0.001757234,0.001786758,0.0003805664],"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":[0.000178341,0.00008290243,0.006399468,0.0004144276,0.0002644585,0.0003666189,0.0002710932,0.6971266,0.002121457,0.2167119,0.001473525,0.07458919],"study_design_scores_gemma":[0.00001145937,0.0000196542,0.0003137108,0.00002507587,0.00001531922,0.0000564969,0.00001523664,0.9450387,0.0002542648,0.05367827,0.0005574138,0.00001429857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003778251,0.0001605608,0.9955318,0.0001102765,0.00000813062,0.00001048241,0.00003390163,0.0001308639,0.0002357188],"genre_scores_gemma":[0.4927088,0.001659014,0.4990879,0.0003340826,0.0002209385,0.0004589178,0.001090123,0.000572297,0.003867932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01113119,"threshold_uncertainty_score":0.05886811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1544555269926568,"score_gpt":0.4374924326685163,"score_spread":0.2830369056758595,"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."}}