{"id":"W2999691300","doi":"10.1002/cjs.11529","title":"Functional measurement error in functional regression","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Functional data analysis; Scalar (mathematics); Observational error; Context (archaeology); Computer science; Linear regression; Linear form; Regression analysis; Mathematics; Conditional expectation; Generalized linear model; Statistics; Applied mathematics; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04526505,0.001197541,0.002074856,0.002371736,0.0008676304,0.002530082,0.002888889,0.002275972,0.003584595],"category_scores_gemma":[0.1783862,0.0007025285,0.001666109,0.003554724,0.005853969,0.003692065,0.003858313,0.003558023,0.000579799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002451536,"about_ca_system_score_gemma":0.002398138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078919,"about_ca_topic_score_gemma":0.005066263,"domain_scores_codex":[0.9599162,0.02966977,0.001316074,0.004310162,0.00385278,0.0009350297],"domain_scores_gemma":[0.8627209,0.110632,0.007304301,0.01081208,0.007778242,0.0007525416],"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":[0.0002075743,0.00005344107,0.02123522,0.000642188,0.0006571832,0.00035632,0.000647295,0.2021024,0.0009096817,0.6419716,0.003853036,0.1273641],"study_design_scores_gemma":[0.00003120281,0.0001337532,0.006841539,0.0002447126,0.0001081997,0.0002366201,0.0001718651,0.5364878,0.0008563198,0.4502027,0.004596123,0.00008913693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0146105,0.0007844247,0.9819773,0.000910683,0.0001269261,0.00003292764,0.0001259264,0.0001429512,0.001288338],"genre_scores_gemma":[0.8078274,0.001106893,0.1849761,0.0006007749,0.0003878096,0.0002338038,0.0005354115,0.0002409357,0.004090966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04526505,"threshold_uncertainty_score":0.2393873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3351494643512912,"score_gpt":0.3350526734503106,"score_spread":0.00009679090098058118,"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."}}