{"id":"W2269665907","doi":"","title":"Dating structural breaks in functional data without dimension reduction","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Functional principal component analysis; Principal component analysis; Functional data analysis; Dimensionality reduction; Dimension (graph theory); Computer science; Monte Carlo method; Feature (linguistics); Mathematics; Algorithm; Econometrics; Statistics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01125278,0.00078574,0.001316953,0.002815229,0.0006881482,0.002122446,0.001881272,0.002275511,0.002946206],"category_scores_gemma":[0.06477058,0.000877332,0.001325369,0.001757504,0.002640883,0.003427314,0.003263279,0.003348205,0.0006729333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007502968,"about_ca_system_score_gemma":0.0009971291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001430526,"about_ca_topic_score_gemma":0.001295969,"domain_scores_codex":[0.9957287,0.002460881,0.0002280802,0.0008586913,0.0004743544,0.0002493309],"domain_scores_gemma":[0.9625446,0.0229689,0.004013565,0.007178797,0.002579328,0.0007148379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005023424,0.0001894401,0.04606135,0.0004398976,0.0006075797,0.0004884874,0.0008637985,0.2976027,0.008636976,0.2829814,0.003813165,0.3578129],"study_design_scores_gemma":[0.00001998309,0.0001582001,0.01477327,0.00007682733,0.0000445485,0.0002592433,0.0001288809,0.8492196,0.001837844,0.131104,0.00228563,0.00009194808],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04791293,0.0002451834,0.9503706,0.0002188145,0.00004795682,0.00005209567,0.0002138902,0.0002221228,0.0007163487],"genre_scores_gemma":[0.7550727,0.0003395526,0.2410421,0.0001763022,0.0001825227,0.0002315024,0.000985386,0.0001918808,0.001777984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01125278,"threshold_uncertainty_score":0.05951107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2258110390439702,"score_gpt":0.202865720463999,"score_spread":0.02294531857997117,"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."}}