{"id":"W4389565577","doi":"10.1214/23-ejs2185","title":"Change-point inference for high-dimensional heteroscedastic data","year":2023,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Heteroscedasticity; Mathematics; Test statistic; Inference; Robustness (evolution); Consistency (knowledge bases); Statistic; Statistics; Point estimation; Statistical hypothesis testing; Algorithm; Econometrics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01700586,0.0006400153,0.001682564,0.002589765,0.0007764942,0.001509508,0.003728892,0.001765127,0.00297271],"category_scores_gemma":[0.09726338,0.0005101978,0.001415984,0.002230272,0.003644589,0.002769311,0.00235218,0.002606637,0.0006332389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006620722,"about_ca_system_score_gemma":0.0008498508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001011652,"about_ca_topic_score_gemma":0.0008023749,"domain_scores_codex":[0.9919463,0.004276993,0.0003175529,0.001846246,0.001274634,0.0003384243],"domain_scores_gemma":[0.9197322,0.06516964,0.005404701,0.005662804,0.00319129,0.0008393528],"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":[0.0007939744,0.0005214639,0.06995135,0.0004482821,0.001105918,0.001005513,0.0008033006,0.2412475,0.006603855,0.4052243,0.003191011,0.2691036],"study_design_scores_gemma":[0.00006865346,0.0003193229,0.008124292,0.00004010705,0.00005864535,0.0001996753,0.00009491169,0.8237301,0.002632382,0.1634567,0.001208598,0.00006670628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0354519,0.0001384978,0.963491,0.0001350433,0.00004550436,0.00004657698,0.00009175386,0.0001751318,0.0004246477],"genre_scores_gemma":[0.734093,0.000157825,0.2636501,0.0002160199,0.0001356839,0.0003622262,0.0005911301,0.00007683651,0.0007171899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01700586,"threshold_uncertainty_score":0.08993667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2094424884710435,"score_gpt":0.4209059546182012,"score_spread":0.2114634661471578,"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."}}