ecmnmle
Mean and covariance of incomplete multivariate normal data
Syntax
Description
ecmnmle(
with no output arguments,
this mode displays the convergence of the ECM algorithm in a plot by estimating
objective function values for each iteration of the ECM algorithm until termination. Data
)
[
estimates the mean and covariance of a data set (Mean
,Covariance
] = ecmnmle(Data
)Data
). If the
data set has missing values, this routine implements the ECM algorithm of Meng and
Rubin [2] with enhancements by Sexton and Swensen [3]. ECM stands for a conditional
maximization form of the EM algorithm of Dempster, Laird, and Rubin [4].
[
adds an optional arguments for Mean
,Covariance
] = ecmnmle(___,InitMethod
,MaxIterations
,Tolerance
,Mean0
,Covar0
)InitMethod
,
MaxIterations
,
Tolerance
,Mean0
, and
Covar0
.
Examples
Input Arguments
Output Arguments
Algorithms
References
[1] Little, Roderick J. A. and Donald B. Rubin. Statistical Analysis with Missing Data. 2nd Edition. John Wiley & Sons, Inc., 2002.
[2] Meng, Xiao-Li and Donald B. Rubin. “Maximum Likelihood Estimation via the ECM Algorithm.” Biometrika. Vol. 80, No. 2, 1993, pp. 267–278.
[3] Sexton, Joe and Anders Rygh Swensen. “ECM Algorithms that Converge at the Rate of EM.” Biometrika. Vol. 87, No. 3, 2000, pp. 651–662.
[4] 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.