{"id":"W2960008033","doi":"10.1002/cjs.11517","title":"Instrumental variable estimation in ordinal probit models with mismeasured predictors","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Tianjin University","keywords":"Covariate; Estimator; Ordinal data; Econometrics; Statistics; Instrumental variable; Probit model; Probit; Observational error; Normality; Errors-in-variables models; Ordinal regression; Variables; Ordered probit; Variable (mathematics); Polychoric correlation; Estimation; Mathematics; Economics; Correlation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004837157,0.0001391345,0.0003211299,0.0002267926,0.00004615082,0.00006835962,0.0001636619,0.00006852896,0.0003889698],"category_scores_gemma":[0.0005819136,0.0001144805,0.00001946137,0.0002231105,0.00007754168,0.0002132927,0.000006247443,0.0002903223,0.000006086553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002563832,"about_ca_system_score_gemma":0.00177642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104156,"about_ca_topic_score_gemma":0.003733034,"domain_scores_codex":[0.9987149,0.00008669059,0.0004944178,0.0001207971,0.0002811831,0.0003020464],"domain_scores_gemma":[0.9985898,0.0003730882,0.0002693927,0.0001302043,0.0002531046,0.0003844603],"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":[0.00006324366,0.00003723181,0.01385885,0.0001524703,0.00004826599,0.000172493,0.0004965869,0.001005674,0.00003119583,0.9744155,0.001370459,0.008347978],"study_design_scores_gemma":[0.001023672,0.0004843289,0.003887903,0.0004431984,0.00006071193,0.0002052237,0.0001765104,0.04520163,0.00003266481,0.9481738,0.00011923,0.0001911893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02504673,0.00001531443,0.9722506,0.0000545523,0.0001981608,0.0002112618,0.000310212,0.000004424474,0.001908737],"genre_scores_gemma":[0.3594506,0.000001218687,0.6404387,0.00002264303,0.00001624492,0.00000189576,0.000004858172,0.00001424609,0.00004950817],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3344039,"threshold_uncertainty_score":0.4668378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03157834562673979,"score_gpt":0.2670234926401559,"score_spread":0.2354451470134161,"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."}}