{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03149974,0.001939111,0.004851057,0.005022127,0.0009057377,0.004028925,0.004994926,0.003060827,0.004588437],"category_scores_gemma":[0.06816251,0.001759011,0.003429614,0.005323735,0.003397342,0.00347238,0.004913535,0.003900284,0.001144464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002333372,"about_ca_system_score_gemma":0.003770696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004528146,"about_ca_topic_score_gemma":0.002594511,"domain_scores_codex":[0.9698433,0.02232149,0.0008348798,0.002314742,0.004025074,0.000660518],"domain_scores_gemma":[0.9593349,0.03312045,0.002038256,0.002323476,0.002836626,0.0003463336],"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":[0.0001499888,0.0001035623,0.0009620725,0.0003689891,0.0006553541,0.0002714512,0.0002014268,0.3724962,0.0006182426,0.4855605,0.004207838,0.1344043],"study_design_scores_gemma":[0.00006484587,0.00005920133,0.0003568912,0.00006810946,0.00008402883,0.00006109591,0.00002358869,0.7010817,0.0003059262,0.2937048,0.004140064,0.00004976421],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007281924,0.0004225344,0.9978788,0.0002464331,0.00003523452,0.00003079555,0.00002941125,0.00009499982,0.0005336992],"genre_scores_gemma":[0.1338066,0.002389275,0.8571796,0.000545442,0.0008929996,0.000822356,0.0005087765,0.0004360445,0.003418884],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03149974,"threshold_uncertainty_score":0.1665885,"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."}}