### Model

The general model is

$$Z\sim N\left(Mean,\text{\hspace{0.17em}}Covariance\right),$$

where each row of `Data`

is an observation
of *Z*.

Each observation of *Z* is assumed to be
iid (independent, identically distributed)
multivariate normal, and missing values are assumed to be missing
at random (MAR).

### Initialization Methods

This routine has three initialization methods that cover most
cases, each with its advantages and disadvantages.

### nanskip

The `nanskip`

method works well with small
problems (fewer than 10 series or with monotone
missing data patterns). It skips over any records with `NaN`

s
and estimates initial values from complete-data records
only. This initialization method tends to yield fastest convergence
of the ECM algorithm. This routine switches to
the `twostage`

method if it determines that significant
numbers of records contain `NaN`

.

### twostage

The `twostage`

method is the best choice for
large problems (more than 10 series). It estimates the
mean for each series using all available data for each series. It
then estimates the covariance matrix with missing values treated
as equal to the mean rather than as `NaN`

s. This
initialization method is quite robust but tends to result in slower
convergence of the ECM algorithm.

### diagonal

The `diagonal`

method is a worst-case approach
that deals with problematic data, such as disjoint series and excessive
missing data (more than 33% missing data). Of the three initialization
methods, this method causes the slowest convergence of the ECM algorithm.