{"id":"W2075601854","doi":"10.1002/cjs.5550350403","title":"Nonresponse weighting adjustment using estimated response probability","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Estimator; Weighting; Inverse probability weighting; Non-response bias; Mathematics; Variance (accounting); Respondent; Probability sampling; Inverse probability; Econometrics; Bayesian probability; Posterior probability","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":[0.0439286,0.001185742,0.002082807,0.003472664,0.0007010127,0.002098827,0.003048686,0.001847398,0.009067626],"category_scores_gemma":[0.2287714,0.0008095002,0.001663379,0.004568956,0.001405417,0.002447279,0.002734534,0.003209839,0.002372902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643628,"about_ca_system_score_gemma":0.001253361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388375,"about_ca_topic_score_gemma":0.000723978,"domain_scores_codex":[0.8698018,0.1110028,0.002957234,0.005051266,0.01030232,0.0008845445],"domain_scores_gemma":[0.8733318,0.08421801,0.007029994,0.02087617,0.01409479,0.0004491468],"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":[0.0004604407,0.0003540372,0.01238684,0.001073139,0.0008103721,0.0001193903,0.0007609795,0.03612788,0.003548277,0.2051895,0.01069463,0.7284745],"study_design_scores_gemma":[0.0006242058,0.001453108,0.04185238,0.001049644,0.0008080201,0.0007082071,0.0005299132,0.4781649,0.02127591,0.3244188,0.1286938,0.0004211477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008782754,0.000401542,0.9858246,0.0005538833,0.0003157154,0.0005818148,0.0001249405,0.000371182,0.003043612],"genre_scores_gemma":[0.2705513,0.0006258197,0.7172443,0.0009663628,0.0004831452,0.00248683,0.0005403713,0.0003750168,0.006726798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0439286,"threshold_uncertainty_score":0.2323195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2125820364407258,"score_gpt":0.3825614725344622,"score_spread":0.1699794360937364,"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."}}