## Smoothly Clipped Absolute Deviation Estimator

### Description

scadEst() applies the SCAD thresholding function of Fan and Li (2001) to each entry of the sample covariance matrix. This penalized estimator constitutes a compromise between hard and soft thresholding of the sample covariance matrix: it is a linear interpolation between soft thresholding up to 2 * lambda and hard thresholding after 3.7 * lambda (Rothman et al. 2009).

### Usage

scadEst(dat, lambda)


### Arguments

 dat A numeric data.frame, matrix, or similar object. lambda A non-negative numeric defining the degree of thresholding applied to each element of dat's sample covariance matrix.

### Value

A matrix corresponding to the estimate of the covariance matrix.

### References

Fan J, Li R (2001). “Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties.” Journal of the American Statistical Association, 96(456), 1348-1360. doi: 10.1198/016214501753382273, https://doi.org/10.1198/016214501753382273.

Rothman AJ, Levina E, Zhu J (2009). “Generalized Thresholding of Large Covariance Matrices.” Journal of the American Statistical Association, 104(485), 177-186. doi: 10.1198/jasa.2009.0101, https://doi.org/10.1198/jasa.2009.0101.

### Examples

scadEst(dat = mtcars, lambda = 0.2)


[Package cvCovEst version 1.1.0 Index]