mvnrobj - Log-likelihood function for multivariate normal regression without missing data

Data

NUMSAMPLES-by-NUMSERIES matrix with NUMSAMPLES samples of a NUMSERIES-dimensional random vector. If a data sample has missing values, represented as NaNs, the sample is ignored. (Use ecmmvnrmle to handle missing data.)

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.

Parameters

NUMPARAMS-by-1 column vector of estimates for the parameters of the regression model.

Covariance

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

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.