sigma_estim_ml: Maximum-Likelihood Covariance Estimation

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

Description

Computes the Maximum-Likelihood estimator of the covariance matrix.

Usage

sigma_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{Σ}=\frac{1}{n} ≤ft(X - \widehat{{μ}} {1} \right)' ≤ft({X} - \widehat{{μ}}{1}\right)

where μ=\bar{x}_{j}=\frac{1}{n}∑_{i=1}^{n}x_{ij} for (for i=1,…, n and j=1,…,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(sp200)
sp_rets <- sp200[,-1]
sigma_ml <- sigma_estim_ml(sp_rets)[[1]]

antshi/CovEstim documentation built on Nov. 13, 2020, 2:25 p.m.