### 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 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.