# mvnrmle

Multivariate normal regression (ignore missing data)

## Syntax

## Description

`[`

estimates a multivariate normal regression model without missing data. The model has
the form`Parameters`

,`Covariance`

,`Resid`

,`Info`

] = mvnrmle(`Data`

,`Design`

)

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

for samples *k* = 1, ... , `NUMSAMPLES`

.

`mvnrmle`

estimates a
`NUMPARAMS`

-by-`1`

column vector of model
parameters called `Parameters`

, and a
`NUMSERIES`

-by-`NUMSERIES`

matrix of
covariance parameters called `Covariance`

.

`mvnrmle(Data, Design)`

with no output arguments plots the
log-likelihood function for each iteration of the algorithm.

`[`

estimates a multivariate normal regression model without missing data using optional
arguments.`Parameters`

,`Covariance`

,`Resid`

,`Info`

] = mvnrmle(___,`MaxIterations`

,`TolParam`

,`TolObj`

,`Covar0`

,`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.

[2] Xiao-Li Meng and Donald B.
Rubin. “Maximum Likelihood Estimation via the ECM Algorithm.”
*Biometrika.* Vol. 80, No. 2, 1993, pp. 267–278.

## Version History

**Introduced in R2006a**

## See Also

`ecmmvnrmle`

| `mvnrstd`

| `mvnrobj`

| `mvregress`

### Topics

- Multivariate Normal Regression
- Least-Squares Regression
- Covariance-Weighted Least Squares
- Feasible Generalized Least Squares
- Seemingly Unrelated Regression
- Multivariate Normal Regression With Missing Data
- Multivariate Normal Regression Without Missing Data
- Capital Asset Pricing Model with Missing Data
- Multivariate Normal Linear Regression