# EM Estimation Options

Using an iterative process, the EM method estimates the means, the covariance matrix, and the correlation of quantitative (scale) variables with missing values.

**Distribution.** EM makes inferences based on the likelihood
under the specified distribution. By default, a normal distribution
is assumed. If you know that the tails of the distribution are longer
than those of a normal distribution, you can request that the procedure
constructs the likelihood function from a Student's *t* distribution
with *n* degrees of freedom. The mixed normal distribution also
provides a distribution with longer tails. Specify the ratio of the
standard deviations of the mixed normal distribution and the mixture
proportion of the two distributions. The mixed normal distribution
assumes that only the standard deviations of the distributions differ.
The means must be the same.

**Maximum iterations.** Sets the maximum number of iterations
to estimate the true covariance. The procedure stops when this number
of iterations is reached, even if the estimates have not converged.

**Save completed data.** You can save a dataset with the imputed
values in place of the missing values. Be aware, though, that covariance-based
statistics using the imputed values will underestimate their respective
parameter values. The degree of underestimation is proportional to
the number of cases that are jointly unobserved.

To Specify EM Options

This feature requires the Missing Values option.

- From the
menus choose:
- In the main Missing Value Analysis dialog box, select the variable(s) for which you want to estimate missing values using the EM method.
- Select EM in the Estimation group.
- To specify predicted and predictor variables, click Variables. See the topic Predicted and Predictor Variables for more information.
- Click EM.
- Select the EM options you want.