{"id":"W6906521179","doi":"10.17877/de290r-20939","title":"Statistical inference for high dimensional panel functional time series","year":2020,"lang":"en","type":"article","venue":"Technische Universität Dortmund Eldorado (Technische Universität Dortmund)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistical inference; High dimensional; Inference; Series (stratigraphy); Dimension (graph theory); Functional data analysis; Time series; Gaussian; Clustering high-dimensional data; Gaussian process","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068352,0.001028254,0.001319512,0.0004696985,0.001174233,0.0001694611,0.001354723,0.0007805625,0.004042779],"category_scores_gemma":[0.001925868,0.001066664,0.0004024088,0.001166664,0.001045703,0.002213119,0.001013762,0.001230526,0.0002741055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178679,"about_ca_system_score_gemma":0.0005480651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009380656,"about_ca_topic_score_gemma":0.00001231651,"domain_scores_codex":[0.9948526,0.0002768826,0.0009271685,0.001511229,0.001151883,0.001280208],"domain_scores_gemma":[0.9940549,0.002749054,0.0005635507,0.001016928,0.0007176939,0.0008978897],"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":[0.001212862,0.0003431974,0.0002885147,0.0002514559,0.0003985192,0.0002555276,0.0002640511,0.00005196089,0.01064827,0.9546348,0.02864567,0.003005166],"study_design_scores_gemma":[0.008998672,0.005201354,0.001558516,0.0005118477,0.003212531,0.0002436716,0.002518135,0.01356891,0.01255115,0.8094169,0.1365057,0.005712585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01439033,0.00006716727,0.9654384,0.003718391,0.0002955961,0.001708888,0.001454366,0.001929322,0.01099751],"genre_scores_gemma":[0.4208676,0.00003961311,0.57364,0.0008037851,0.0002890861,0.00003578387,0.0005003043,0.0001585729,0.003665294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4064772,"threshold_uncertainty_score":0.9991783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07762118269382683,"score_gpt":0.2898860725828685,"score_spread":0.2122648898890416,"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."}}