{"id":"W2050662955","doi":"10.1016/j.laa.2014.08.008","title":"Estimation of parameters in the extended growth curve model via outer product least squares for covariance","year":2014,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Project 211; Shanghai University of Finance and Economics; National Natural Science Foundation of China; Shanghai Leading Academic Discipline Project; Ryerson University","keywords":"Mathematics; Estimator; Covariance; Generalized least squares; Estimation of covariance matrices; Applied mathematics; M-estimator; Least-squares function approximation; Growth curve (statistics); Statistics; Covariance matrix; Wishart distribution","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.003130443,0.001120458,0.001698007,0.0006399842,0.0004608,0.001530648,0.001740224,0.001285152,0.001600296],"category_scores_gemma":[0.01294375,0.001022088,0.001463707,0.001251121,0.001641825,0.003004374,0.001869075,0.002516697,0.0006565508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009657242,"about_ca_system_score_gemma":0.00301667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01458397,"about_ca_topic_score_gemma":0.01021665,"domain_scores_codex":[0.9985939,0.0005965139,0.00006015675,0.0003863396,0.0002505264,0.0001125771],"domain_scores_gemma":[0.995181,0.003294833,0.00042018,0.0004817645,0.0005250335,0.00009706073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005553166,0.00003577767,0.001271526,0.00006277397,0.00006516179,0.00003952805,0.00009607463,0.9315624,0.001832235,0.02600413,0.0006369469,0.03833799],"study_design_scores_gemma":[0.000003297701,0.000009755739,0.0003070904,0.000004625028,0.000005673723,0.00001280115,0.000006818493,0.9878023,0.0002913738,0.01128164,0.000262451,0.00001221127],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0109172,0.00005039878,0.9885424,0.00006669397,0.000007714219,0.00001043535,0.00006587793,0.0001241142,0.0002152058],"genre_scores_gemma":[0.4637162,0.000458728,0.524981,0.00012234,0.00006225729,0.0002757514,0.001132057,0.000636308,0.008615296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01458397,"threshold_uncertainty_score":0.0289982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01573371082245691,"score_gpt":0.2522044296455377,"score_spread":0.2364707188230808,"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."}}