Evaluate standard errors for multivariate normal regression model
[StdParameters, StdCovariance] = ecmmvnrstd(Data, Design,
Covariance, Method, CovarFormat)


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



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

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

[StdParameters, StdCovariance] = ecmmvnrstd(Data, Design,
Covariance, Method, CovarFormat)
evaluates standard errors
for a multivariate normal regression model with missing data. The
model has the form
$$Dat{a}_{k}\sim N\left(Desig{n}_{k}\times Parameters,\text{\hspace{0.17em}}Covariance\right)$$
for samples k = 1, ... , NUMSAMPLES
.
ecmmvnrstd
computes two
outputs:
StdParameters
is a NUMPARAMS
by1
column
vector of standard errors for each element of Parameters
,
the vector of estimated model parameters.
StdCovariance
is a NUMSERIES
byNUMSERIES
matrix
of standard errors for each element of Covariance
,
the matrix of estimated covariance parameters.
Note

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.
See Multivariate Normal Regression, LeastSquares Regression, CovarianceWeighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.
Roderick J. A. Little and Donald B. Rubin, Statistical Analysis with Missing Data, 2nd ed., John Wiley & Sons, Inc., 2002.