{"id":"W2979977316","doi":"10.2139/ssrn.3444996","title":"Dealing with the Log of Zero in Regression Models","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Estimator; Ordinary least squares; Poisson regression; Poisson distribution; Zero (linguistics); Mathematics; Iterated function; Log-linear model; Transformation (genetics); Applied mathematics; Generalized linear model; Computer science; Linear model; Statistics; Population","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001642995,0.00007673323,0.0001708263,0.00003975373,0.00003631914,0.00001005113,0.0001605044,0.00003593746,0.0000285819],"category_scores_gemma":[0.0001325751,0.00003748192,0.00003064168,0.00008751816,0.00003047221,0.00005369496,0.00001933631,0.0008986975,0.000002800885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009184,"about_ca_system_score_gemma":0.0003932733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002171801,"about_ca_topic_score_gemma":0.0000736324,"domain_scores_codex":[0.998791,0.0001004458,0.0002016351,0.00008263142,0.0001853233,0.000639013],"domain_scores_gemma":[0.9991444,0.0004817099,0.0001559989,0.000138365,0.00005627904,0.00002325296],"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":[0.00005178553,0.00001924202,0.0004432648,0.00001047314,0.00001718569,9.069551e-7,0.0001325403,0.0001453034,0.0002808895,0.986689,0.00001173394,0.01219768],"study_design_scores_gemma":[0.0003375894,0.0002282182,0.00007917752,0.0001188357,0.00001198872,0.00006088155,0.0004643173,0.003708677,0.0002220265,0.994698,0.00001481372,0.00005541402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.400188,0.0003500005,0.596439,0.0002576985,0.00002717636,0.00009627656,6.834201e-7,0.00000493175,0.002636184],"genre_scores_gemma":[0.9777384,0.0002789529,0.02164134,0.00002239021,0.00001689534,0.000001352489,1.064634e-7,0.00001097578,0.0002895641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5775504,"threshold_uncertainty_score":0.3904443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03984268718694223,"score_gpt":0.3359776156038848,"score_spread":0.2961349284169426,"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."}}