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\name{builtin}
\alias{.baggedMeanCov}
\alias{.bayesSteinMeanCov}
\alias{.cov.arw}
\alias{.cov.nnve}
\alias{.cov.shrink}
\alias{.donostahMeanCov}
\alias{.ledoitWolfMeanCov}
\alias{.rmtMeanCov}
\alias{.studentMeanCov}
\title{Estimation of Mean and Covariances of Asset Sets}
\description{
Helper functions for estimating the mean and/or covariance matrix of a
time series of assets by traditional and robust methods.
}
\usage{
.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, ...)
}
\arguments{
\item{x}{
any rectangular time series object which can be converted by the
function \code{as.matrix()} into a matrix object, e.g. like an
object of class \code{timeSeries}, \code{data.frame}, or \code{mts}.
}
\item{baggedR}{
when \code{methode="bagged"}, an integer value, the number of
bootstrap replicates, by default 100.
}
\item{center}{
specifies for a data set (n x p), the initial location
estimator(1 x p).
}
\item{cov}{
Initial scatter estimator (p x p).
}
\item{alpha}{
Maximum thresholding proportion (optional scalar, default:
\code{alpha = 0.025}).
}
\item{pcrit}{
critical value for outlier probability (optional scalar, default
values from simulations).
}
\item{datamat}{
a matrix in which each row represents an observation or
point and each column represents a variable.
}
\item{k}{
desired number of nearest neighbors (default is 12).
}
\item{pnoise}{
percent of added noise
}
\item{emconv}{
convergence tolerance for EM.
}
\item{bound}{
value used to identify surges in variance caused by outliers
wrongly included as signal points (bound = 1.5 means a 50
percent increase).
}
\item{extension}{
whether or not to continue after reaching the last chi-square
distance. The default is to continue, which is indicated by
setting \code{extension= TRUE}.
}
\item{devsm}{
when \code{extension = TRUE}, the algorithm stops if the
relative difference in variance is less than devsm (default is
0.01).
}
\item{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 \code{lambda=0} the empirical correlations are
recovered.
}
\item{verbose}{
a logical indicating whether to print progress
information to the stdout.
}
\item{\dots}{
optional arguments to be passed to the underlying estimators.
For details we refer to the manual pages of the functions
\code{cov.rob} in the R package \code{MASS}, to the functions
\code{covMcd} and \code{covOGK} in the R package
\code{robustbase}.
}
}
\value{
The functions return a list with elements containing the covariance
and mean. The list may contain additional control parameters.
}
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