July 2, 2024Open Access

Asymptotics of estimators for structured covariance matrices

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HLHendrik P. LopuhaäMaterials innovation institute

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Implication

Theoretical analysis reveals limiting variances for structured covariance matrix estimators across multivariate models, highlighting scalar comparisons of asymptotic relative efficiency.

Key Points

Limiting variance of structured covariance matrix estimators follows a scaled projection of radial random matrices, extending directly to variance components estimators.
Analysis of linear covariance structures enables comparing asymptotic relative efficiency and gross error sensitivity across multivariate models using single scalars.
Findings characterize influence functions and breakdown points for S-estimators, supporting simultaneous robustness and efficiency tuning via cutoff adjustments.

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Hendrik P. Lopuhaä (2024) studied this question.

synapsesocial.com/papers/68e61b7fb6db6435875ae465 https://doi.org/10.48550/arxiv.2407.01974

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Implication

Theoretical analysis reveals limiting variances for structured covariance matrix estimators across multivariate models, highlighting scalar comparisons of asymptotic relative efficiency.

Asymptotics of estimators for structured covariance matrices

Discussion

Member takes

Implication

Key Points

Cite This Study

Asymptotics of estimators for structured covariance matrices

Discussion

Member takes

Implication

Key Points

Cite This Study

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