{"id":"W2138227681","doi":"10.1002/cjs.10021","title":"Bayesian robust transformation and variable selection: A unified approach","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Transformation (genetics); Variable (mathematics); Feature selection; Mathematics; Regression analysis; Markov chain Monte Carlo; Linear regression; Computer science; Bayesian probability; Markov chain; Scale (ratio); Statistics; Econometrics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004078983,0.0001099494,0.0002410998,0.0001418072,0.0001504931,0.00005675494,0.00007490841,0.00006640098,0.00005578143],"category_scores_gemma":[0.0005491934,0.0001008309,0.00002040708,0.0001550333,0.00005235656,0.0001636327,0.000001379419,0.0002313587,3.72978e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008601193,"about_ca_system_score_gemma":0.0004291906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001168003,"about_ca_topic_score_gemma":0.0004752487,"domain_scores_codex":[0.9990764,0.00006637425,0.0004021175,0.00008682369,0.0001322402,0.0002360158],"domain_scores_gemma":[0.9987826,0.0002398654,0.000163409,0.00006539889,0.0002534276,0.0004953496],"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":[0.00001373327,0.00001660479,0.00001145352,0.00004742151,0.00001625713,0.00002110589,0.000545681,0.001035961,0.00002512069,0.9729888,0.001905937,0.02337187],"study_design_scores_gemma":[0.0003964103,0.0002328651,0.00014906,0.00003742977,0.00007232263,0.0003038957,0.000192362,0.03687444,0.00001841823,0.9597208,0.001872801,0.0001291726],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001492749,0.0000589066,0.9948944,0.0001606987,0.00007389385,0.00009932816,0.0001397293,0.000006180041,0.004417605],"genre_scores_gemma":[0.07800515,0.00001385885,0.9216577,0.0001245164,0.00006801462,7.546998e-7,0.000005236996,0.00001057107,0.0001142354],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07785588,"threshold_uncertainty_score":0.4111763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0705293880238148,"score_gpt":0.3133953685021057,"score_spread":0.2428659804782909,"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."}}