mvnrstd
Evaluate standard errors for multivariate normal regression model
Syntax
[StdParameters,StdCovariance] = mvnrstd(Data,Design,Covariance,CovarFormat)
Arguments
|
|
| 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:
|
Description
[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 aNUMPARAMS
-by-1
column vector of standard errors for each element ofParameters
, the vector of estimated model parameters.StdCovariance
is aNUMSERIES
-by-NUMSERIES
matrix of standard errors for each element ofCovariance
, the matrix of estimated covariance parameters.Note
mvnrstd
operates slowly when you calculate the standard errors associated with the covariance matrixCovariance
.
Notes
You can configure Design
as a matrix if NUMSERIES
= 1
or as a cell array if NUMSERIES
≥ 1
.
If
Design
is a cell array andNUMSERIES
=1
, each cell contains aNUMPARAMS
row vector.If
Design
is a cell array andNUMSERIES
>1
, each cell contains aNUMSERIES
-by-NUMPARAMS
matrix.
Examples
See Multivariate Normal Regression, Least-Squares Regression, Covariance-Weighted Least Squares, Feasible Generalized Least Squares, and Seemingly Unrelated Regression.
References
Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing Data. 2nd Edition. John Wiley & Sons, Inc., 2002.
Version History
Introduced in R2006a