Robust covariance and precision matrix estimators. Based on the review of P.-L. Loh and X. L. Tan. (2018)
There are in total 4 robust covariance and 3 correlation estimation implemented, namely:
corSpearman: Spearman correlation
corKendall: Kendall's tau
corQuadrant: Quadrant correlation coefficients
covGKmat: Gnanadesikan-Kettenring estimator by Tarr et al. (2015) and Oellerer and Croux (2015)
covSpearmanU: SpearmanU covariance estimator by P.-L. Loh and X. L. Tan. (2018), The pairwise covariance matrix estimator proposed in Oellerer and Croux (2015), where the MAD estimator is combined with Spearman’s rho
covOGK: Orthogonalized Gnanadesikan-Kettenring (OGK) estimator by Maronna, R. A. and Zamar, R. H. (2002)
covNPD: Nearest Positive (semi)-Definite projection of the pairwise covariance matrix estimator considered in Tarr et al. (2015).
P.-L. Loh and X. L. Tan. (2018) then used these robust estimates in Graphical Lasso (package
glasso) or Quadratic Approximation (package
QUIC) to obtain sparse solutions to precision matrix
glasso, a function
robglasso stand for robust graphical LASSO is implemented. It has build in cross validation described in P.-L. Loh and X. L. Tan. (2018), for instance, to use the method with cross validation:
robglasso(data=matrix(rnorm(100),20,5), covest = cov,CV=TRUE)
data should be a matrix and
covest should be a function that estimate the covariance e.g. anyone mentioned above. The result list contains everything from
glasso output with the optimal tuning parameter found by cross validation. One can also decide fold by setting
robglasso. For more details see
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