# mvnrstd

Evaluate standard errors for multivariate normal regression model

## Syntax

## Description

`[`

evaluates standard errors for a multivariate normal regression model without missing
data. The model has the form`StdParameters`

,`StdCovariance`

] = mvnrstd(`Data`

,`Design`

,`Covariance`

)

$$Dat{a}_{k}\sim N\left(Desig{n}_{k}\times Parameters,\text{\hspace{0.17em}}Covariance\right)$$

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`

.

`[`

computes the log-likelihood function based on current maximum likelihood parameter
estimates without missing data using an optional argument.`StdParameters`

,`StdCovariance`

] = mvnrstd(___,`CovarFormat`

)

## Input Arguments

## Output Arguments

## References

[1] 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**