cov_estim_ml: Maximum-Likelihood Covariance Estimation

In antshi/CovEstim: (High-dimensional) Covariance Estimation

Maximum-Likelihood Covariance Estimation

Description

Computes the Maximum-Likelihood (ML) estimator of the covariance matrix.

Usage

cov_estim_ml(data)

Arguments

data	an nxp data matrix.

Details

The Maximum-Likelihood estimator of the covariance matrix for a data matrix X is computed with the following formula:

\hat{\Sigma}=\frac{1}{n} \left(X - \widehat{{\mu}} {1} \right)' \left({X} - \widehat{{\mu}}{1}\right)

where \mu=\bar{x}_{j}=\frac{1}{n}\sum_{i=1}^{n}x_{ij} for (for i=1,\ldots, n and j=1,\ldots,p) is the sample mean vector and 1 is an 1xp vector of ones.

Value

a list with the following entries

• a pxp estimated covariance matrix.

• an estimation specific tuning parameter, here an NA.

Examples

data(rets_m)
sigma_ml <- cov_estim_ml(rets_m)[[1]]

antshi/CovEstim documentation built on June 10, 2025, 3:11 a.m.