Outliers free data via the reweighted MCD: Outliers free data via the reweighted MCD

Description

Outliers free data via the reweighted MCD.

Usage

rmcd(x, alpha = NULL)

Value

A list including:

poia: A vector with the indices of the vectors that were removed.
x: The outlier free data.

Arguments

x: A numerical matrix with the variables. If you have a data.frame (i.e. categorical data) turn them into a matrix using data.frame.to_matrix.
alpha: A number controlling the size of the subsets over which the determinant is minimized; roughly alpha*n observations are used for computing the determinant. Values between 0.5 and 1 are allowed.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Details

The FEDHC algorithm.

References

Rousseeuw P. J. and Leroy A. M. (1987) Robust Regression and Outlier Detection. Wiley.

Rousseeuw P. J. and van Driessen K. (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41: 212-223.

Pison G., Van Aelst S., and Willems G. (2002) Small Sample Corrections for LTS and MCD, Metrika 55: 111-123.

Hubert M., Rousseeuw P. J. and Verdonck, T. (2012) A deterministic algorithm for robust location and scatter. Journal of Computational and Graphical Statistics 21: 618-637.

Cerioli A. (2010). Multivariate outlier detection with high-breakdown estimators.Journal of the American Statistical Association 105(489): 147-156.

Cerchiello P. and Giudici P. (2016). Big data analysis for financial risk management. Journal of Big Data 3(1): 18.

Examples

Run this code

x <- matrix( rnorm(200 * 20), nrow = 200 )
x1 <- matrix( rnorm(10 * 20, 10), nrow = 10 )
x <- rbind(x, x1)
a <- pchc::rmcd(x)
a$poia

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