The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Robust Estimation of Dispersion Matrices and Principal Components
Abstract
This paper uses Monte Carlo methods to compare the performances of several robust procedures for estimating a correlation matrix and its principal components. The estimators are formed either from separate bivariate analyses or by simultaneous manipulation of all variables by using techniques such as multivariate trimming and M-estimation. The M-estimators stand up exceptionally well. They and the multivariate trimming procedure are especially effective at estimating the principal components, including a near singularity. However, the M-estimators can break down relatively easily when the dimensionality is large and the outliers are asymmetric. With missing data, the element-wise approach becomes more attractive.
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Cites
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Analysis of a complex of statistical variables into principal components.
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A General Qualitative Definition of Robustness
Hampel, F. R. (1971) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177693054 · Cited by 1,002
Robust $M$-Estimators of Multivariate Location and Scatter
Maronna, R. A. (1976) · The Annals of Statistics · doi:10.1214/aos/1176343347 · Cited by 952
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Sen, P. K., & Gnanadesikan, R. (1978) · Journal of the American Statistical Association · doi:10.2307/2286626 · Cited by 897
Self-citationRobust Estimates, Residuals, and Outlier Detection with Multiresponse Data
Gnanadesikan, R., & Kettenring, J. R. (1972) · Biometrics · doi:10.2307/2528963 · Cited by 808
Self-citationNew Light on the Correlation Coefficient and its Transforms
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Cited by
Bro, R., & Smilde, A. K. (2014) · Anal. Methods · doi:10.1039/c3ay41907j · Cited by 3,038
Dimensionality assessment of ordered polytomous items with parallel analysis
Timmerman, M. E., & Lorenzo-Seva, U. (2011) · Psychological Methods · doi:10.1037/a0023353 · Cited by 1,634
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194
Nielsen, A. A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.888195 · Cited by 746
(2004) · Springer Texts in Statistics · doi:10.1007/b98963 · Cited by 517
Metadata
- Authors
- Devlin, S. J., Gnanadesikan, R., & Kettenring, J. R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1981-06
- Volume
- 76
- Issue
- 374
- Pages
- 354-362
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2052740976
- Cited by
- 391
- References
- 15
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
