Initial mean and covariance
[Mean,Covariance] = ecmninit(Data,InitMethod)
(Optional) Character vector that identifies one of three
defined initialization methods to compute initial estimates for the
mean and covariance of the data. If
[Mean,Covariance] = ecmninit(Data,InitMethod) creates
initial mean and covariance estimates for the function
1 column vector
estimate for the mean of
estimate for the covariance of
This routine has three initialization methods that cover most cases, each with its advantages and disadvantages.
nanskip method works well with small
problems (fewer than 10 series or with monotone missing data patterns).
It skips over any records with
NaNs 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
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
NaNs. This initialization method
is robust but tends to result in slower convergence of the ECM algorithm.
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.