Maximum Likelihood Estimation Via the ECM Algorithm:
Computing the Asymptotic Variance
David A. van Dyk and Xiao-Li Meng
Department of Statistics, The University of Chicago,
Chicago, IL 60637, U.S.A.
Donald B. Rubin
Department of Statistics, Harvard University, Cambridge, MA
02138, U.S.A.
This paper provides detailed theory, algorithms, and illustrations for
computing asymptotic variance-covariance matrices for maximum
likelihood estimates using the ECM algorithm (Meng and
Rubin, 1993). This Supplemented ECM (SECM) algorithm is developed as
an extension of the Supplemented EM (SEM) algorithm (Meng and Rubin, 1991a).
Explicit examples are given, including one that demonstrates SECM,
like SEM, has a powerful internal error detecting system for the
implementation of the parent ECM or in SECM itself.
Key words: Contingency Table; Convergence Rate; EM
Algorithm; Fisher Information; Incomplete Data; IPF; Missing Data;
SEM Algorithm
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