{"id":"W3134868418","doi":"10.1002/cjs.11610","title":"The conditional distance autocovariance function","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Autocovariance; Autoregressive model; Partial autocorrelation function; Autocorrelation; Mathematics; Series (stratigraphy); Time series; Conditional expectation; Conditional variance; Nonlinear system; STAR model; Econometrics; Statistics; Applied mathematics; Algorithm; Autoregressive integrated moving average; Autoregressive conditional heteroskedasticity; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002828526,0.00006233747,0.0001918998,0.00006954849,0.000328475,0.0001725161,0.0001214148,0.0000270351,0.001452496],"category_scores_gemma":[0.0002655809,0.00005895587,0.00007285561,0.0001930348,0.0000764978,0.0000904587,0.000005479777,0.0001169063,0.00008803783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001251845,"about_ca_system_score_gemma":0.000422545,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128445,"about_ca_topic_score_gemma":0.02850887,"domain_scores_codex":[0.9991519,0.00001473813,0.0005290872,0.00009269483,0.0000383308,0.0001732498],"domain_scores_gemma":[0.9988925,0.0000924192,0.0004142072,0.0001438683,0.000262198,0.0001948173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003628154,0.000003333688,0.004043535,0.000005589214,0.00008556727,0.00007692943,0.00004250754,0.0001947998,0.000001023045,0.966034,0.02851684,0.0009922585],"study_design_scores_gemma":[0.0001313358,0.0000246218,0.0284967,0.000008236164,0.00001129342,0.00005363633,0.0001003841,0.0009982549,0.000001415251,0.2144779,0.7556285,0.00006766999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008073553,0.00909532,0.9742864,0.001963303,0.001543568,0.00005368611,0.002519767,0.000002948589,0.009727686],"genre_scores_gemma":[0.9890574,0.0001235755,0.004099254,0.0002572612,0.0003172668,0.000002498615,0.00004682041,0.00001305072,0.006082909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98825,"threshold_uncertainty_score":0.9994603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276860014782373,"score_gpt":0.1835707025691618,"score_spread":0.1608021024213381,"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."}}