Covariance Estimation for Multivariate t Distribution
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.
cov.trob(x, wt = rep(1, n), cor = FALSE, center = TRUE, nu = 5, maxit = 25, tol = 0.01)
- data matrix. Missing values (NAs) are not allowed.
- A vector of weights for each case: these are treated as if the case
- Flag to choose between returning the correlation (
cor = TRUE) or covariance (
cor = FALSE) matrix.
- 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 = TRUEthe MLE of the location vector is used.
degrees of freedomfor the multivariate t distribution. Must exceed 2 (so that the covariance matrix is finite).
- Maximum number of iterations in fitting.
- Convergence tolerance for fitting.
- 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
wtargument 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.
J. T. Kent, D. E. Tyler and Y. Vardi (1994) A curious likelihood identity for the multivariate t-distribution. Communications in Statistics---Simulation and Computation 23, 441--453.
Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer.