{"id":"W2162166782","doi":"10.1002/cjs.11189","title":"Nonparametric estimation of mean and covariance structures for longitudinal data","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Covariance; Asymptotic distribution; Consistency (knowledge bases); Nonparametric regression; Smoothing; Statistics; Mathematics; Normality; Econometrics; Longitudinal data; Covariance function; Analysis of covariance; Estimation; Applied mathematics; Computer science; Data mining; Estimator; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0004392176,0.00009074819,0.0002797535,0.0001632717,0.00006259972,0.00006109946,0.0002492383,0.0000455641,0.0001844494],"category_scores_gemma":[0.01052348,0.00007699785,0.00001548333,0.0001267094,0.0001479624,0.000127622,0.00001671651,0.0001120739,9.268724e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003212092,"about_ca_system_score_gemma":0.0004153733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261873,"about_ca_topic_score_gemma":0.001370625,"domain_scores_codex":[0.9990651,0.00004179142,0.0004736546,0.0001074517,0.0001393097,0.0001726997],"domain_scores_gemma":[0.9963368,0.002227063,0.0003858473,0.0002232627,0.0005166804,0.0003103277],"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":[0.00001260134,0.00001294412,0.001177123,0.0003189095,0.0000700319,0.00001508945,0.0002109173,0.0001098784,0.00002351031,0.8261681,0.02500941,0.1468714],"study_design_scores_gemma":[0.0003203478,0.0002025187,0.01654021,0.00006566859,0.0001056464,0.00006008585,0.00006429658,0.05364,0.00004729541,0.9286649,0.0001880029,0.0001010155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01272234,0.000139345,0.9847063,0.00007929819,0.0001679526,0.0001623376,0.001967701,0.000001500494,0.00005327261],"genre_scores_gemma":[0.3066483,0.000008965659,0.6932592,0.00001880641,0.00003379555,0.000001175328,0.00001100169,0.000007953204,0.0000107967],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2939259,"threshold_uncertainty_score":0.9978113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2171591424896952,"score_gpt":0.3689315664249095,"score_spread":0.1517724239352143,"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."}}