{"id":"W3208809677","doi":"10.1016/j.jmva.2021.104864","title":"Robust functional principal component analysis for non-Gaussian longitudinal data","year":2021,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Renmin University of China","keywords":"Functional principal component analysis; Principal component analysis; Mathematics; Gaussian; Skewness; Functional data analysis; Focus (optics); Gaussian process; Component (thermodynamics); Inference; Function (biology); Applied mathematics; Algorithm; Statistics; Econometrics; Artificial intelligence; Computer science","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":[0.001920072,0.0002399912,0.001319352,0.0006194192,0.000186022,0.00008339016,0.0004300999,0.000104551,0.000375786],"category_scores_gemma":[0.002318403,0.0001895594,0.001080285,0.001761482,0.00005010939,0.000310734,0.0002229676,0.0003003641,0.000001860038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024778,"about_ca_system_score_gemma":0.000163028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000288699,"about_ca_topic_score_gemma":0.0001914427,"domain_scores_codex":[0.997052,0.0002417754,0.001236858,0.0004923872,0.0006457221,0.0003312529],"domain_scores_gemma":[0.9948286,0.00202891,0.0009989005,0.0008397897,0.001041115,0.0002626789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001504539,0.003905952,0.02830877,0.0003471283,0.1959255,0.00110963,0.0007123373,0.6682194,0.005563734,0.08127573,0.001948377,0.01117893],"study_design_scores_gemma":[0.001760827,0.00009674919,0.05689573,0.0000342023,0.05414732,0.00004724841,0.0002009802,0.8545989,0.0002689036,0.03059879,0.0009938939,0.000356433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01214189,0.00007209594,0.9866914,0.0003932937,0.0001687059,0.0000986109,0.0003205508,0.000009963196,0.000103461],"genre_scores_gemma":[0.322291,0.00002307297,0.6768994,0.00003565833,0.0002317942,0.000004054246,0.0001833113,0.00001695982,0.0003147139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3101491,"threshold_uncertainty_score":0.7730008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3250310491438873,"score_gpt":0.4447132969272161,"score_spread":0.1196822477833288,"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."}}