{"id":"W4391999235","doi":"10.1002/cjs.11804","title":"Censored autoregressive regression models with Student‐<i>t</i> innovations","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Censoring (clinical trials); Missing data; Robustness (evolution); Autoregressive model; Computer science; Statistics; Expectation–maximization algorithm; Robust regression; Censored regression model; Asymptotic distribution; Linear regression; Regression analysis; Econometrics; Least absolute deviations; Mathematics; Regression; Maximum likelihood; Estimator","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":[],"consensus_categories":[],"category_scores_codex":[0.0003444415,0.000173381,0.0003089806,0.0003448071,0.000138109,0.0002226142,0.000228501,0.00007244339,0.0002077493],"category_scores_gemma":[0.0007615125,0.0001166891,0.00003739199,0.0003407224,0.0001832358,0.0001869794,0.00001067842,0.0004090343,0.000007150231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001660473,"about_ca_system_score_gemma":0.001709448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001849322,"about_ca_topic_score_gemma":0.001484972,"domain_scores_codex":[0.9985917,0.00008463659,0.0005266254,0.0001484614,0.0003480031,0.0003005965],"domain_scores_gemma":[0.9974772,0.001011751,0.0002234022,0.0001711555,0.0006464979,0.0004699514],"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":[0.00000982089,0.00001417991,0.0001604547,0.00009196431,0.0000781141,0.001637466,0.001267093,0.00002188275,0.00001839145,0.9428037,0.03448642,0.01941054],"study_design_scores_gemma":[0.0002431371,0.0001875207,0.0007093793,0.001025966,0.0001361243,0.0003229221,0.0004299678,0.004204289,0.00004338778,0.9897135,0.002793977,0.0001898257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00128111,0.0003120301,0.9947262,0.0003802861,0.0004980961,0.0001086549,0.0008342003,0.00001750495,0.001841929],"genre_scores_gemma":[0.1489063,0.00002070178,0.8505458,0.00008057818,0.0001438887,0.000003086484,0.000008021817,0.00003467483,0.0002570415],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1476251,"threshold_uncertainty_score":0.4758441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05392270525402309,"score_gpt":0.3536866070369255,"score_spread":0.2997639017829024,"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."}}