A Proposal for Robpca Algorithm

Authors

  • Hasan Bulut Department of Statistics, University of Ondokuz May?s, Samsun, Turkey.
  • Y Department of Statistics, University of Ondokuz May?s, Samsun, Turkey.
  • S Department of Banking and Finance, University of Giresun, Giresun, Turkey.

Keywords:

ROBPCA, Robust Principal Component Analysis, Standardization, High Dimensional Data.

Abstract

Principal component Analysis (PCA) is one of the most frequently used multivariate statistical methods. Especially, it is used on the purpose of dimension reduction and obtaining uncorrelated variables. However, classic PCA (CPCA) is sensitive to outlier. Because it is based on classic covariance or correlation matrices influenced by outliers.

References

Alpar R. Applied Multivariate Statistical Methods. Ankara: Detay Publishing, 2011.

Bulut H.,

Koch I. Analysis of Multivariate and High-Dimensional Data. New York: Cambridge University Press, 2014.

Rencher A. C. Methods of Multivariate Analysis. New York: John Wiley & Sons; 2003.

Tatl?dil H. Applied Multivarite Statistical Analysis. Ankara: Akademi Publishing, 1996.

Hubert M., Rousseeuw P. J., Branden K. V.

Filzmoser P. ,Todorov V.

Rousseeuw P. J.

Rousseeuw P. J., van Driessen K.

Friedman, J. H., Tukey, J. W.

Huber P. J.

Li G.Y., Cheng P.

Croux C., Grazen R.

Filzmoser P., Serneels S., Croux C. et al.

Moller S. F., Frese J. V., Bro R.,

Hubert, M., Rousseeuw, P., Verdonck, T.

Erba? S. O. Probability and Statistics. Ankara: Gazi Publishing, 2008.

Maronna R. A., Martin R. D., Yohai, V. J. Robust statistics. Chichester: John Wiley & Sons, 2006.

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Published

2016-08-23

How to Cite

Bulut, H., Y, & S, . (2016). A Proposal for Robpca Algorithm. International Journal of Sciences: Basic and Applied Research (IJSBAR), 29(2), 119–129. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/6131

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