Covariance Estimation for Multivariate t Distribution
Usage
cov.trob(x, wt=rep(1, n), cor=FALSE, center=TRUE, nu=5, maxit=25, tol=0.01)
Arguments
x
|
data matrix. Missing values (NAs) are not allowed.
|
wt
|
A vector of weights for each case: these are treat as if the case i
actually occurred wt[i] times.
|
cor
|
Flag to choose between returning the correlation (cor=TRUE ) or
covariance (cor=FALSE ) matrix.
|
center
|
a logical value or a numeric vector providing the location about which
the covariance is to be taken. If center=FALSE , no centering is done;
if center=TRUE the MLE of the location vector is used.
|
nu
|
"degrees of freedom" for the multivariate t distribution. Must exceed
2 (so that the covariance matrix is finite).
|
maxit
|
Maximum number of iterations in fitting.
|
tol
|
Convergence tolerance for fitting.
|
Description
Estimates a covariance or correlation matrix assuming the data came
from a multivariate t distribution: this provides some degree of
robustness to outlier without giving a high breakdown point.Value
A list with the following components
cov
|
the fitted covariance matrix.
|
center
|
the estimated or specified location vector.
|
wt
|
the specified weights: only returned if the wt argument was given.
|
n.obs
|
the number of cases used in the fitting.
|
cor
|
the fitted correlation matrix: only returned if cor = TRUE .
|
call
|
The matched call.
|
iter
|
The number of iterations used.
|
References
J. T. Kent and D. E. Tyler and Y. Vardi (1994)
A curious likelihood identity for the multivariate t-distribution.
Communications in StatisticsSimulation and Computation,
23, 441-453.See Also
cov
, cov.wt
, cov.mve
Examples
data(stackloss)
cov.trob(stackloss)