{"id":"W1514683365","doi":"","title":"OPTIMAL INFERENCE METHODS IN LINEAR MODELS WITH CHANGE-POINTS","year":2014,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Estimator; Mathematics; Context (archaeology); Linear regression; Applied mathematics; Linear model; Regression analysis; Inference; Shrinkage estimator; Statistics; Econometrics; Minimax estimator; Computer science; Minimum-variance unbiased estimator","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01614393,0.001633931,0.003284052,0.002309439,0.0007223626,0.00253615,0.003102035,0.002801807,0.002894994],"category_scores_gemma":[0.05565025,0.001794224,0.002314691,0.002975911,0.00312214,0.003622684,0.002959609,0.004447566,0.000667748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002423457,"about_ca_system_score_gemma":0.002725255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00827046,"about_ca_topic_score_gemma":0.005914547,"domain_scores_codex":[0.989171,0.006992865,0.0004187775,0.001972481,0.001082803,0.0003621347],"domain_scores_gemma":[0.9627604,0.03319588,0.001566846,0.001054207,0.001185285,0.0002374309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001119031,0.0001177704,0.002724856,0.0005508774,0.0005187083,0.0002603684,0.0002704997,0.6095126,0.0004414692,0.300351,0.002194042,0.08294591],"study_design_scores_gemma":[0.00003138965,0.00003878948,0.0003418206,0.00004296914,0.00003844107,0.00002678503,0.00002924378,0.8515806,0.0001528454,0.1462106,0.001486081,0.00002047287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003127366,0.001331478,0.9940194,0.0005083751,0.00006627256,0.00004212247,0.0000591309,0.0001179665,0.0007279193],"genre_scores_gemma":[0.2907945,0.005888125,0.6900483,0.0008308645,0.001045284,0.001083064,0.0009472531,0.0002981889,0.009064338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01614393,"threshold_uncertainty_score":0.08537829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488893638046909,"score_gpt":0.3743000511748124,"score_spread":0.2254106873701215,"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."}}