{"id":"W2513369585","doi":"10.5539/ijsp.v5n5p43","title":"Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CUSUM; Mathematics; Exponential function; Change detection; Point (geometry); Point estimation; Exponential family; Exponential growth; Applied mathematics; Estimation; Statistics; Computerized adaptive testing; Computer science; Econometrics; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.007203108,0.000954693,0.001124597,0.0009923053,0.0004183602,0.000711941,0.001583946,0.001157325,0.001608073],"category_scores_gemma":[0.04210219,0.000379992,0.0007736112,0.0009052248,0.001178844,0.001462274,0.0009172253,0.00150334,0.0003745029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005686355,"about_ca_system_score_gemma":0.001229587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004164688,"about_ca_topic_score_gemma":0.002818835,"domain_scores_codex":[0.9968356,0.00144787,0.0001635165,0.0006393619,0.0006879405,0.0002256667],"domain_scores_gemma":[0.9761825,0.01865922,0.001210974,0.001154012,0.002534611,0.0002586967],"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":[0.0009461532,0.0002144033,0.02448158,0.0003281308,0.0004649534,0.0006546309,0.0003525651,0.642958,0.01359595,0.03190123,0.001495513,0.2826069],"study_design_scores_gemma":[0.00001478372,0.000110267,0.003890693,0.0000115636,0.00002933616,0.00007527346,0.00002823225,0.9877945,0.002976574,0.004608519,0.0004324362,0.00002786112],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05557874,0.0001697587,0.9435731,0.00008656746,0.00003196688,0.00004734439,0.00003800719,0.000187939,0.0002865117],"genre_scores_gemma":[0.7727656,0.0003007557,0.2241794,0.00008879796,0.00009683587,0.0001945402,0.0004199638,0.0001417381,0.001812359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007203108,"threshold_uncertainty_score":0.0380941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1719953749774735,"score_gpt":0.4022801862745174,"score_spread":0.2302848112970439,"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."}}