{"id":"W2045722221","doi":"10.1016/j.jspi.2007.05.028","title":"Connections of the Poisson weight function to overdispersion and underdispersion","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Mathematical Inequalities and Applications","field":"Mathematics","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Overdispersion; Mathematics; Poisson distribution; Pointwise; Weight function; Function (biology); Quasi-likelihood; Applied mathematics; Poisson regression; Statistics; Mathematical analysis; Count data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02880698,0.001335232,0.003909279,0.004279976,0.002173116,0.005439727,0.008128291,0.004865148,0.009645606],"category_scores_gemma":[0.2084451,0.002178414,0.00301384,0.005380815,0.01195245,0.01734592,0.006360445,0.007835026,0.0006892968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003052112,"about_ca_system_score_gemma":0.003107292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006886588,"about_ca_topic_score_gemma":0.00351021,"domain_scores_codex":[0.9884651,0.005745501,0.0007139776,0.002512602,0.001449302,0.001113477],"domain_scores_gemma":[0.7485126,0.2072862,0.01685961,0.01715147,0.006556224,0.003633856],"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":[0.00007158134,0.00003702438,0.002978942,0.00009112996,0.00008428329,0.0001643405,0.0003506237,0.0290606,0.0002607492,0.9521775,0.001483843,0.01323932],"study_design_scores_gemma":[0.00001684628,0.00001948733,0.0008274877,0.00002487621,0.00003390387,0.0001327181,0.00006500618,0.06542481,0.0001412631,0.9323525,0.0009201971,0.00004093063],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0706135,0.002000432,0.9149024,0.004129644,0.0002912285,0.000084068,0.0004068149,0.0002659514,0.007305954],"genre_scores_gemma":[0.8344691,0.005098774,0.1402711,0.002478503,0.002121634,0.000396968,0.0006972495,0.0009039862,0.01356257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02880698,"threshold_uncertainty_score":0.1523476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06063624366315861,"score_gpt":0.3724164393088247,"score_spread":0.3117801956456661,"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."}}