{"id":"W2791397484","doi":"10.1002/wics.1432","title":"Estimation and testing for separable variance–covariance structures","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Covariance; Mathematics; Kronecker product; Variance (accounting); Covariance matrix; Estimator; Statistics; Multivariate normal distribution; Separable space; Normality; Estimation of covariance matrices; Rational quadratic covariance function; Applied mathematics; Multivariate statistics; Covariance intersection; Kronecker delta","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0815649,0.002283469,0.003814017,0.003685079,0.002145149,0.00442729,0.004413171,0.003261829,0.004587126],"category_scores_gemma":[0.3562835,0.001714665,0.003987814,0.004536342,0.008458167,0.007526383,0.005382594,0.005610669,0.0008219594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001319427,"about_ca_system_score_gemma":0.004111554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00259719,"about_ca_topic_score_gemma":0.001787883,"domain_scores_codex":[0.8766958,0.08758375,0.005185327,0.01524229,0.01246257,0.00283035],"domain_scores_gemma":[0.446804,0.5078546,0.01284773,0.02393873,0.006421736,0.002133234],"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":[0.001970401,0.0008874789,0.2044868,0.0009510496,0.006863068,0.002123852,0.004756522,0.08637858,0.005813242,0.337517,0.005225266,0.3430268],"study_design_scores_gemma":[0.0002781123,0.001001386,0.05072521,0.0002916258,0.0003823337,0.0007433401,0.001375404,0.4100426,0.004593355,0.5261251,0.004167834,0.0002737053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.190086,0.0003883442,0.8031841,0.0009154154,0.0001089312,0.0003599564,0.000658363,0.000431863,0.003867009],"genre_scores_gemma":[0.7541882,0.000319019,0.2409427,0.0003205004,0.0001553707,0.001097578,0.001794158,0.0001415127,0.001040911],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0815649,"threshold_uncertainty_score":0.4313616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.25947355111828,"score_gpt":0.5121512017751574,"score_spread":0.2526776506568774,"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."}}