{"id":"W2024095064","doi":"10.5539/ijsp.v2n3p101","title":"A New Algorithm for Detecting Outliers in Linear Regression","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Outlier; Mathematics; Mahalanobis distance; Estimator; Covariance; Statistics; Algorithm; Covariance matrix; Linear regression; Computation; Monte Carlo method; Robust regression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002534341,0.001574151,0.001877324,0.002322228,0.0009319236,0.001907885,0.002800602,0.001983807,0.003941549],"category_scores_gemma":[0.009503795,0.0009538247,0.001536148,0.002218846,0.001046859,0.002457109,0.002226444,0.00293861,0.00384029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007059255,"about_ca_system_score_gemma":0.00148345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002674524,"about_ca_topic_score_gemma":0.001883043,"domain_scores_codex":[0.9964624,0.0006986452,0.0002773957,0.0009228975,0.001430461,0.0002083026],"domain_scores_gemma":[0.9965096,0.001344407,0.0003478704,0.0004007111,0.001302242,0.0000952824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002828786,0.0001053152,0.001701653,0.0003370082,0.0001753956,0.000203492,0.0002419792,0.1645618,0.02371535,0.01949184,0.006400421,0.7827827],"study_design_scores_gemma":[0.00004115517,0.00009274452,0.0004513439,0.00003263634,0.00002748712,0.0001989837,0.00003426038,0.9703985,0.009842389,0.007812227,0.01101637,0.00005178775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005555485,0.00004615105,0.9987099,0.00002044079,0.0000263036,0.00002212819,0.00001589643,0.0004920859,0.0001115815],"genre_scores_gemma":[0.01455019,0.0001076531,0.9833822,0.00006863182,0.00006226885,0.0001780867,0.0001828781,0.0002702118,0.001197868],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003941549,"threshold_uncertainty_score":0.01340306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07914750690543301,"score_gpt":0.4210058444409948,"score_spread":0.3418583375355617,"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."}}