{"id":"W1984072874","doi":"10.1016/j.jspi.2012.10.009","title":"An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select optimal lag","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Unit root; Estimator; Mathematics; Lag; Selection (genetic algorithm); Model selection; Limit (mathematics); Statistics; Stationary process; Plot (graphics); Applied mathematics; Mathematical optimization; Computer science; Artificial intelligence; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0007125511,0.0002459591,0.0004900874,0.0001833353,0.0001635731,0.0001134868,0.0001613473,0.00007999954,0.00005247593],"category_scores_gemma":[0.003326393,0.0002027689,0.00002550115,0.0001484922,0.0001689423,0.0006329424,0.00007239573,0.0004186433,0.000003322149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003807165,"about_ca_system_score_gemma":0.00009646743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002543921,"about_ca_topic_score_gemma":0.00000141607,"domain_scores_codex":[0.9980391,0.0002508859,0.0006572838,0.0002140845,0.0004396109,0.0003990966],"domain_scores_gemma":[0.990145,0.008391803,0.0003039108,0.0001180606,0.0003327178,0.000708502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001022023,0.000469479,0.2530378,0.0002217162,0.0003199046,0.0001005168,0.005993698,0.001668287,0.0002231776,0.6780462,0.00103699,0.05786023],"study_design_scores_gemma":[0.001136126,0.001966818,0.6915256,0.0003123008,0.0002085898,0.00006777429,0.0003701763,0.03907123,0.00007290969,0.2648183,0.00003769541,0.0004124546],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4461869,0.00007125489,0.5531539,0.00003715393,0.00008943693,0.00006949351,0.000255333,0.000012521,0.0001239238],"genre_scores_gemma":[0.6318137,0.00001025031,0.3679958,0.00002764005,0.0001147424,0.000003319076,0.00001610322,0.00001429179,0.000004202552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4384879,"threshold_uncertainty_score":0.8268676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2072641134721543,"score_gpt":0.4585828778034047,"score_spread":0.2513187643312504,"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."}}