Loglikelihood function for multivariate normal regression without missing data
Objective = mvnrobj(Data, Design, Parameters, Covariance,
CovarFormat)


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





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

Objective = mvnrobj(Data, Design, Parameters, Covariance,
CovarFormat)
computes the loglikelihood function based
on current maximum likelihood parameter estimates without missing
data. Objective
is a scalar that contains the loglikelihood
function.
You can configure Design
as a matrix if NUMSERIES
= 1
or as a cell array if NUMSERIES
≥ 1
.
If Design
is a cell array and NUMSERIES
=
1, each cell contains a NUMPARAMS
row
vector.
If Design
is a cell array and NUMSERIES
> 1, each cell contains a NUMSERIES
byNUMPARAMS
matrix.
Although Design
should not have NaN
values,
ignored samples due to NaN
values in Data
are
also ignored in the corresponding Design
array.
See Multivariate Normal Regression, LeastSquares Regression, CovarianceWeighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.