Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data-Reference-Cited by-同舟云学术

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

Published:2022-07-08 Issue: Volume: Page:1-19
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Zahariah Siti¹²,Midi Habshah²³

Affiliation:

1. Applied Statistics and Data Science Cluster, Universiti Kuala Lumpur Malaysian Institute of Information Technology (UniKL MIIT), Kuala Lumpur, Malaysia

2. Institute for Mathematical Research, Universiti Putra Malaysia, Serdang, Malaysia

3. Department of Mathematics and Statistics, Universiti Putra Malaysia, Serdang, Malaysia

Funder

Universiti Putra Malaysia Grant under Putra Grant

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2022.2093842

Reference39 articles.

1. Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination

2. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data

3. On the Applications of Robust PCA in Image and Video Processing

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