The expectationmaximization (EM) algorithm is a technique for maximum likelihood estimation in parametric models for incomplete
data. The EM statement uses the EM algorithm to compute the MLE for , the means and covariance matrix, of a multivariate normal distribution from the input data set with missing values. Either
the means and covariances from complete cases or the means and standard deviations from available cases can be used as the
initial estimates for the EM algorithm. You can also specify the correlations for the estimates from available cases.
You can also use the EM statement with the NIMPUTE=0 option in the PROC MI statement to compute the EM estimates without multiple
imputation, as shown in Example 63.1.
The following seven options are available with the EM statement (in alphabetical order):

CONVERGE=p
XCONV=p

sets the convergence criterion. The value must be between 0 and 1. The iterations are considered to have converged when the
change in the parameter estimates between iteration steps is less than p for each parameter—that is, for each of the means and covariances. For each parameter, the change is a relative change if
the parameter is greater than 0.01 in absolute value; otherwise, it is an absolute change. By default, CONVERGE=1E–4.

INITIAL=CC  AC  AC(R=r)

sets the initial estimates for the EM algorithm. The INITIAL=CC option uses the means and covariances from complete cases;
the INITIAL=AC option uses the means and standard deviations from available cases, and the correlations are set to zero; and
the INITIAL=AC( R= r) option uses the means and standard deviations from available cases with correlation r, where and p is the number of variables to be analyzed. The default is INITIAL=AC.

ITPRINT

prints the iteration history in the EM algorithm.

MAXITER=number

specifies the maximum number of iterations used in the EM algorithm. The default is MAXITER=200.

OUT=SASdataset

creates an output SAS data set that contains results from the EM algorithm. The data set contains all variables in the input
data set, with missing values being replaced by the expected values from the EM algorithm. See the section Output Data Sets for a description of this data set.

OUTEM=SASdataset

creates an output SAS data set of TYPE=COV that contains the MLE of the parameter vector . These estimates are computed with the EM algorithm. See the section Output Data Sets for a description of this output data set.

OUTITER <( options )> =SASdataset

creates an output SAS data set of TYPE=COV that contains parameters for each iteration. The data set includes a variable named
_Iteration_
to identify the iteration number. The parameters in the output data set depend on the options specified. You can specify
the MEAN and COV options to output the mean and covariance parameters. When no options are specified, the output data set
contains the mean parameters for each iteration. See the section Output Data Sets for a description of this data set.