ecmmvnrstd - Evaluate standard errors for multivariate normal regression model

Data

NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. Missing values are represented as NaNs. Only samples that are entirely NaNs are ignored. (To ignore samples with at least one NaN, use mvnrstd.)

Design

A matrix or a cell array that handles two model structures:

• If NUMSERIES = 1, Design is a NUMSAMPLES-by-NUMPARAMS matrix with known values. This structure is the standard form for regression on a single series.

• If NUMSERIES ≥ 1, Design is a cell array. The cell array contains either one or NUMSAMPLES cells. Each cell contains a NUMSERIES-by-NUMPARAMS matrix of known values.

  If Design has a single cell, it is assumed to have the same Design matrix for each sample. If Design has more than one cell, each cell contains a Design matrix for each sample.

Covariance

NUMSERIES-by-NUMSERIES matrix of estimates for the covariance of the regression residuals.

Method

(Optional) String that identifies method of calculation for the information matrix:

• hessian - Default method. Use the expected Hessian matrix of the observed log-likelihood function. This method is recommended since the resultant standard errors incorporate the increased uncertainties due to missing data.

• fisher - Use the Fisher information matrix.

CovarFormat

(Optional) String that specifies the format for the covariance matrix. The choices are:

• 'full' - Default method. The covariance matrix is a full matrix.

• 'diagonal' - The covariance matrix is a diagonal matrix.