An Empirical Comparison of EM Initialization Methods and Model Choice Criteria for Mixtures of Skew-Normal Distributions
2012
We investigate, via simulation study, the performance of the EM algorithm
for maximum likelihood estimation in finite mixtures of skew-normal
distributions with component specific parameters. The study takes into account
the initialization method, the number of iterations needed to attain a
fixed stopping rule and the ability of some classical model choice criteria to
estimate the correct number of mixture components. The results show that
the algorithm produces quite reasonable estimates when using the method
of moments to obtain the starting points and that, combining them with the
AIC, BIC, ICL or EDC criteria, represents a good alternative to estimate the
number of components of the mixture. Exceptions occur in the estimation
of the skewness parameters, notably when the sample size is relatively small,
and in some classical problematic cases, as when the mixture components
are poorly separated.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
43
References
11
Citations
NaN
KQI