The GEM Algorithm Nancy L. Glenn, Ph. D. USC Department of Statistics Columbia, SC Abstract
The
expectation maximization (EM) algorithm is a general iterative
algorithm that employs parametric procedures for maximum likelihood
estimation for missing data analysis. Since the EM algorithm uses
parametric procedures, strong assumptions are made about the underlying
distribution of the data. The GEM (Glenn EM) algorithm uses a
nonparametric likelihood, empirical likelihood, for determining the
maximum likelihood estimator in missing data problems. The GEM
algorithm's advantage is that it makes few assumptions regarding the
underlying distribution of the data. Using simulated data, a salinity
data set, and the well{studied seed data found in Snedecor's 1956
paper, I show that the GEM algorithm provides convergence rates and
estimation values that are comparable to those of the EM algorithm. I
also investigate the GEM algorithm's robustness to initial estimates of
starting values.
