{"id":"W2892257438","doi":"10.1111/rssb.12370","title":"Testing Relevant Hypotheses in Functional Time Series via Self-Normalization","year":2020,"lang":"en","type":"preprint","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Normalization (sociology); Null hypothesis; Series (stratigraphy); Statistical hypothesis testing; Computer science; Mathematics; Sample size determination; Applied mathematics; Algorithm; Statistics","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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00539284,0.0008532685,0.002347017,0.0000931281,0.0003602222,0.0002052702,0.001023541,0.0008731544,0.001214901],"category_scores_gemma":[0.1172795,0.0006089572,0.0005442119,0.000545826,0.001214455,0.0001706843,0.001341092,0.003657329,0.00003073517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004968232,"about_ca_system_score_gemma":0.0008124344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007885341,"about_ca_topic_score_gemma":0.00001175122,"domain_scores_codex":[0.9888043,0.005128533,0.002961893,0.0008253176,0.001368524,0.0009114796],"domain_scores_gemma":[0.9517208,0.04437585,0.001874578,0.0005608525,0.0009514392,0.0005164284],"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":[0.002111769,0.0009319314,0.002765519,0.003460781,0.001436213,0.0004722811,0.001877514,0.002291689,0.001233658,0.9125416,0.04792625,0.02295075],"study_design_scores_gemma":[0.0006407791,0.0007526253,0.02766532,0.0004324045,0.0007553277,0.0002162616,0.0001800726,0.0341758,0.0001652687,0.93351,0.0008604492,0.0006456926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001428976,0.0001341003,0.9918612,0.002507326,0.001646846,0.0006087936,0.001097241,0.0001130043,0.0006025402],"genre_scores_gemma":[0.004912814,0.00004902773,0.9932559,0.0005809305,0.0007921504,0.00003999431,0.00003310957,0.0001202256,0.0002159032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1118867,"threshold_uncertainty_score":0.9996981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1841176453208196,"score_gpt":0.361875283298843,"score_spread":0.1777576379780234,"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."}}