adaptiveLassoEst {cvCovEst} | R Documentation |
adaptiveLassoEst()
applied the adaptive LASSO to the
entries of the sample covariance matrix. The thresholding function is
inspired by the penalized regression introduced by
Zou (2006). The thresholding function assigns
a weight to each entry of the sample covariance matrix based on its
initial value. This weight then determines the relative size of the penalty
resulting in larger values being penalized less and reducing bias
(Rothman et al. 2009).
adaptiveLassoEst(dat, lambda, n)
dat |
A numeric |
lambda |
A non-negative |
n |
A non-negative |
A matrix
corresponding to the estimate of the covariance
matrix.
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.
Zou H (2006).
“The Adaptive Lasso and Its Oracle Properties.”
Journal of the American Statistical Association, 101(476), 1418–1429.
doi: 10.1198/016214506000000735, https://doi.org/10.1198/016214506000000735.
adaptiveLassoEst(dat = mtcars, lambda = 0.9, n = 0.9)