Maximum Likelihood Estimation and Model
Comparison for Mixtures of Structural Equation
Models with Ignorable Missing Data |
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Authors: | Sik-Yum Lee Xin-Yuan
Song |
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Institution: | (1) The Chinese University of Hong Kong, China;(2) Zhongshan University, China |
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Abstract: | The objective of this paper is to develop the maximum likelihood approach
for analyzing a finite mixture of structural equation models with missing data that are
missing at random. A Monte Carlo EM algorithm is proposed for obtaining the maximum
likelihood estimates. A well-known statistic in model comparison, namely the Bayesian
Information Criterion (BIC), is used for model comparison. With the presence of missing
data, the computation of the observed-data likelihood function value involved in the BIC
is not straightforward. A procedure based on path sampling is developed to compute this
function value. It is shown by means of simulation studies that ignoring the incomplete
data with missing entries gives less accurate ML estimates. An illustrative real example is
also presented. |
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Keywords: | |
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