{"id":"W4384071184","doi":"10.1080/07474938.2023.2225947","title":"Automatic variable selection for semiparametric spatial autoregressive model","year":2023,"lang":"en","type":"article","venue":"Econometric Reviews","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Science Foundation of Hunan Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Autoregressive model; Estimator; Parametric statistics; Semiparametric model; Applied mathematics; Model selection; Semiparametric regression; Mathematics; Monte Carlo method; Moment (physics); Selection (genetic algorithm); Parametric model; Mathematical optimization; Computer science; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002373767,0.0004902376,0.0008715134,0.0009607797,0.0002558363,0.0007021888,0.0009692883,0.0005959322,0.001256995],"category_scores_gemma":[0.007400817,0.0003812781,0.0007564231,0.001044007,0.0006178498,0.001099903,0.001122324,0.0009719237,0.00034179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003591736,"about_ca_system_score_gemma":0.0006810935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001016729,"about_ca_topic_score_gemma":0.0008412228,"domain_scores_codex":[0.9977121,0.001608902,0.00005543346,0.0002331402,0.0003133809,0.00007695954],"domain_scores_gemma":[0.9966831,0.002562401,0.0002620793,0.0002246595,0.0002293334,0.00003839536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001028215,0.00006163607,0.003660864,0.0003795871,0.0002300068,0.0002091246,0.0002348224,0.4493792,0.004555508,0.2486468,0.003418052,0.2891216],"study_design_scores_gemma":[0.0000148976,0.00002171872,0.0006198375,0.00001553888,0.00001525089,0.0000448414,0.00001073391,0.9438412,0.0007285027,0.05312615,0.001546634,0.00001466969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006159749,0.0003577543,0.9929503,0.00008169779,0.00001145652,0.000007642087,0.00002379469,0.00009845785,0.0003090997],"genre_scores_gemma":[0.5183299,0.00195534,0.4757482,0.0001609698,0.0002145827,0.000274089,0.0005821525,0.0001971403,0.002537762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002373767,"threshold_uncertainty_score":0.01255387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.106206180436186,"score_gpt":0.2822936655441785,"score_spread":0.1760874851079924,"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."}}