{"id":"W1987648679","doi":"10.1016/j.jmva.2012.02.007","title":"Estimation of parameters in the growth curve model via an outer product least squares approach for covariance","year":2012,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Shanghai University of Finance and Economics; National Natural Science Foundation of China","keywords":"Mathematics; Estimator; Estimation of covariance matrices; Covariance matrix; Covariance; Applied mathematics; Generalized least squares; Statistics; Asymptotic distribution; Least-squares function approximation; Autocovariance; Mathematical analysis","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":[0.003707086,0.0001301604,0.0005352807,0.0002682718,0.00004253798,0.00003027902,0.0002667856,0.00005242339,0.000005816477],"category_scores_gemma":[0.003143108,0.00007815447,0.0002385286,0.0005391911,0.0000471789,0.0003423594,0.00001601305,0.0001765866,2.394275e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000309418,"about_ca_system_score_gemma":0.00002880952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008666381,"about_ca_topic_score_gemma":0.000004526275,"domain_scores_codex":[0.9981081,0.0004606372,0.000763174,0.0001288795,0.0003350087,0.0002041994],"domain_scores_gemma":[0.9974261,0.00117805,0.0007619358,0.0002504464,0.0003149483,0.00006851947],"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":[0.0008789672,0.005554771,0.01631333,0.00067786,0.003385798,0.000004516407,0.02197058,0.6988567,0.002684931,0.2050723,0.0001915225,0.04440875],"study_design_scores_gemma":[0.000387736,0.0001007086,0.006152178,0.00001799543,0.001163118,0.000005435741,0.0002108113,0.9232987,0.0005597906,0.06800888,8.068818e-7,0.00009382675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08616267,0.00002976413,0.9134361,0.00009162256,0.00003190256,0.000183175,0.00001895539,0.000002922199,0.00004291638],"genre_scores_gemma":[0.5067123,0.000002081305,0.4932165,0.00001864609,0.00003136419,0.00000746607,0.000003017512,0.00000536912,0.00000328577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4205496,"threshold_uncertainty_score":0.3762821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394602210292045,"score_gpt":0.4005410472701715,"score_spread":0.261080826240967,"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."}}