builtin | R Documentation |
Helper functions for estimating the mean and/or covariance matrix of a time series of assets by traditional and robust methods.
.baggedMeanCov(x, baggedR = 100, ...)
.bayesSteinMeanCov(x, ...)
.cov.arw(x, center, cov, alpha = 0.025, pcrit = NULL)
.cov.nnve(datamat, k = 12, pnoise = 0.05, emconv = 0.001, bound = 1.5,
extension = TRUE, devsm = 0.01)
.cov.shrink(x, lambda, verbose = FALSE)
.donostahMeanCov(x, ...)
.ledoitWolfMeanCov(x, ...)
.rmtMeanCov(x, ...)
.studentMeanCov(x, ...)
x |
any rectangular time series object which can be converted by the
function |
baggedR |
when |
center |
specifies for a data set (n x p), the initial location estimator(1 x p). |
cov |
Initial scatter estimator (p x p). |
alpha |
Maximum thresholding proportion (optional scalar, default:
|
pcrit |
critical value for outlier probability (optional scalar, default values from simulations). |
datamat |
a matrix in which each row represents an observation or point and each column represents a variable. |
k |
desired number of nearest neighbors (default is 12). |
pnoise |
percent of added noise |
emconv |
convergence tolerance for EM. |
bound |
value used to identify surges in variance caused by outliers wrongly included as signal points (bound = 1.5 means a 50 percent increase). |
extension |
whether or not to continue after reaching the last chi-square
distance. The default is to continue, which is indicated by
setting |
devsm |
when |
lambda |
the correlation shrinkage intensity (range 0-1). If lambda is
not specified (the default) it is estimated using an analytic
formula from Schaefer and Strimmer (2005) - see details
below. For |
verbose |
a logical indicating whether to print progress information to the stdout. |
... |
optional arguments to be passed to the underlying estimators.
For details we refer to the manual pages of the functions
|
The functions return a list with elements containing the covariance and mean. The list may contain additional control parameters.
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