What question did this study set out to answer?

This research aims to improve the estimation of mean and scatter matrix in high-dimensional multivariate data, particularly in the presence of outliers.

February 12, 2026International Statistical Review

Robust Adaptive Estimation of Mean and Scatter Matrix in High‐Dimensional Settings

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This research aims to improve the estimation of mean and scatter matrix in high-dimensional multivariate data, particularly in the presence of outliers.
Introduced the minimum weighted covariance determinant (MWCD) estimator and its regularised version (MRWCD).
Developed adaptive versions of these estimators with a sample extension.
Evaluated properties through extensive simulations and real sample data analysis.
Adaptive weights for the MRWCD estimator showed advantages over standard methods.
Illustrated lower sensitivity to local outliers compared to the regularised MCD estimator.
Demonstrated effective distinction of group-specific structure in a two-group example.

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Implication

This research demonstrates improved mean and scatter estimation in high dimensions, suggesting more robust methods for handling outliers.

Cite This Study

Kalina et al. (2026) studied this question.

synapsesocial.com/papers/698d6dc15be6419ac0d52f6e https://doi.org/10.1111/insr.70025

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Robust Adaptive Estimation of Mean and Scatter Matrix in High‐Dimensional Settings

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Discussion

Implication

Cite This Study

Robust Adaptive Estimation of Mean and Scatter Matrix in High‐Dimensional Settings

Key Points

Discussion

Implication

Cite This Study

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