{"id":"W1966109447","doi":"10.1080/10618600.2000.10474871","title":"Note on “Obtaining the Maximum Likelihood Estimates in Incomplete<i>R</i>×<i>C</i>Contingency Tables Using a Poisson Generalized Linear Model”","year":2000,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Contingency table; Mathematics; Macro; Statistics; Poisson distribution; Standard error; Table (database); Generalized linear model; Missing data; Applied mathematics; Design matrix; Maximum likelihood; Algorithm; Linear model; Computer science; Data mining","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":[0.0008273426,0.0002004724,0.0004495078,0.0001403086,0.0001991067,0.00007684471,0.0001680506,0.00007768159,0.00008802584],"category_scores_gemma":[0.0009071971,0.0001333621,0.000073628,0.0002712576,0.0001644091,0.00008239321,0.00002904973,0.0004388809,0.000001243469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002869889,"about_ca_system_score_gemma":0.00009954708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003468304,"about_ca_topic_score_gemma":0.00001464999,"domain_scores_codex":[0.9979975,0.000199068,0.0008553159,0.0001689248,0.0004969942,0.0002822027],"domain_scores_gemma":[0.9951205,0.004088881,0.0003247149,0.00008574123,0.0002410812,0.0001391129],"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":[0.0003866073,0.0003251827,0.001501231,0.00009689319,0.0000732019,0.0001062843,0.000597474,0.04843631,0.0001012877,0.8115237,0.0003018966,0.13655],"study_design_scores_gemma":[0.0004554899,0.0001237168,0.001598854,0.00009789217,0.00003715087,0.00004924651,0.0000110893,0.4446732,0.000006106873,0.5528222,0.00003321625,0.00009176252],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1258541,0.00008035271,0.8731925,0.0005042831,0.00005229013,0.00009946588,0.0001399221,0.000008143637,0.00006890336],"genre_scores_gemma":[0.1845876,0.00005820119,0.8149039,0.0003380215,0.00008651034,0.000001704939,0.000005442185,0.00001567911,0.000002920266],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3962369,"threshold_uncertainty_score":0.5438347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04626384178380755,"score_gpt":0.3564908719570994,"score_spread":0.3102270301732918,"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."}}