Mean and covariance of incomplete multivariate normal data
[Mean,Covariance] = ecmnmle(Data,InitMethod,MaxIterations,Tolerance,Mean0,Covar0)
(Optional) Character vector that identifies one of three
defined initialization methods to compute initial estimates for the
mean and covariance of the data. If
(Optional) Maximum number of iterations for the expectation
conditional maximization (ECM) algorithm. Default =
(Optional) Convergence tolerance for the ECM algorithm
[Mean,Covariance] = ecmnmle(Data,InitMethod,MaxIterations,Tolerance,Mean0,Covar0) estimates
the mean and covariance of a data set. If the data set has missing
values, this routine implements the ECM algorithm of Meng and Rubin
 with enhancements by Sexton and Swensen . ECM stands for expectation conditional maximization, a conditional
maximization form of the EM algorithm of Dempster, Laird, and Rubin
This routine has two operational modes.
With no output arguments, this mode displays the convergence of the ECM algorithm. It estimates and plots objective function values for each iteration of the ECM algorithm until termination, as shown in the following plot.
Display mode can determine
or serve as a diagnostic tool. The objective function is the negative
log-likelihood function of the observed data and convergence to a
maximum likelihood estimate corresponds with minimization of the objective.
With output arguments, this mode estimates the mean and covariance via the ECM algorithm.
To see an example of how to use
run the program
The general model is
where each row of
Data is an observation
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). See Little and Rubin  for a precise definition of MAR.
This routine estimates the mean and covariance from given data. If data values are missing, the routine implements the ECM algorithm of Meng and Rubin  with enhancements by Sexton and Swensen .
If a record is empty (every value in a sample is
this routine ignores the record because it contributes no information.
If such records exist in the data, the number of nonempty samples
used in the estimation is ≤
The estimate for the covariance is a biased maximum likelihood
estimate (MLE). To convert to an unbiased estimate, multiply the covariance
Count – 1), where
Count is the number of nonempty
samples used in the estimation.
This routine requires consistent values for
It must have enough nonmissing values to converge. Finally, it must
have a positive-definite covariance matrix. Although the references
provide some necessary and sufficient conditions, general conditions
for existence and uniqueness of solutions in the missing-data case,
do not exist. The main failure mode is an ill-conditioned covariance
matrix estimate. Nonetheless, this routine works for most cases that
have less than 15% missing data (a typical upper bound for financial
This routine has three initialization methods that cover most cases, each with its advantages and disadvantages. The ECM algorithm always converges to a minimum of the observed negative log-likelihood function. If you override the initialization methods, you must ensure that the initial estimate for the covariance matrix is positive-definite.
The following is a guide to the supported initialization methods.
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% of data missing). Of the three initialization
methods, this method causes the slowest convergence of the ECM algorithm.
If problems occur with this method, use display mode to examine convergence
and modify either
or try alternative initial estimates with
If all else fails, try
Mean0 = zeros(NumSeries); Covar0 = eye(NumSeries,NumSeries);
Given estimates for mean and covariance from this routine, you
can estimate standard errors with the companion routine
The ECM algorithm does not work for all patterns of missing values. Although it works in most cases, it can fail to converge if the covariance becomes singular. If this occurs, plots of the log-likelihood function tend to have a constant upward slope over many iterations as the log of the negative determinant of the covariance goes to zero. In some cases, the objective fails to converge due to machine precision errors. No general theory of missing data patterns exists to determine these cases. An example of a known failure occurs when two time series are proportional wherever both series contain nonmissing values.
 Little, Roderick J. A. and Donald B. Rubin. Statistical Analysis with Missing Data. 2nd Edition. John Wiley & Sons, Inc., 2002.
 Meng, Xiao-Li and Donald B. Rubin. “Maximum Likelihood Estimation via the ECM Algorithm.” Biometrika. Vol. 80, No. 2, 1993, pp. 267–278.
 Sexton, Joe and Anders Rygh Swensen. “ECM Algorithms that Converge at the Rate of EM.” Biometrika. Vol. 87, No. 3, 2000, pp. 651–662.
 Dempster, A. P., N. M. Laird, and Donald B. Rubin. “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society. Series B, Vol. 39, No. 1, 1977, pp. 1–37.