Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data

DOI10.1080/02664763.2022.2093842OpenAlexW4284976754WikidataQ114100822 ScholiaQ114100822MaRDI QIDQ6063297

Unnamed Author, Habshah Midi

Publication date: 7 November 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2022.2093842

zbMATH Keywords

principal component analysishigh dimensional dataminimum regularized covariance determinantrobust Mahalanobis distancehigh leverage point

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• The minimum regularized covariance determinant estimator
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• Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination
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• Principal component analysis.
• Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
• The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression
• The masking and swamping effects using the planted mean-shift outliers models
• Outlier detection for high-dimensional data
• Identifying multiple influential observations in linear regression
• Alternatives to the Median Absolute Deviation

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