Evaluate standard errors for multivariate normal regression model
[StdParameters,StdCovariance] = mvnrstd(Data,Design,Covariance,CovarFormat)
|
|
| A matrix or a cell array that handles two model structures:
|
|
|
| (Optional) Character vector that specifies the format for the covariance matrix. The choices are:
|
[StdParameters,StdCovariance] = mvnrstd(Data,Design,Covariance,CovarFormat)
evaluates
standard errors for a multivariate normal regression model without
missing data. The model has the form
for samples k = 1, ... , NUMSAMPLES
.
mvnrstd
computes two outputs:
StdParameters
is a NUMPARAMS
-by-1
column
vector of standard errors for each element of Parameters
,
the vector of estimated model parameters.
StdCovariance
is a NUMSERIES
-by-NUMSERIES
matrix
of standard errors for each element of Covariance
,
the matrix of estimated covariance parameters.
Note
mvnrstd
operates slowly when you calculate the standard
errors associated with the covariance matrix
Covariance
.
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
-by-NUMPARAMS
matrix.
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.
Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing Data. 2nd Edition. John Wiley & Sons, Inc., 2002.