Model-Based Clustering of Multistate Data with Latent Change: An Application with DHS Data
Finite mixture modeling has been used extensively as a model-based clustering technique. This research addresses the application of mixture models to multistate data (sequences of states) under the Markov assumption. By assuming a latent or hidden Markov process, the model incorporates the estimation of the misclassification error. The data are from the life history calendar from the Brazilian Demographic and Health Survey (DHS) 1996 in which contraceptive use dynamics are surveyed retrospectively. The results show that the dynamics are heterogeneous with two subpopulations.
The author would like to thank the Fundação para a Ciência e a Tecnologia (Portugal) for its financial support (PTDC/CS-DEM/108033/2008). I also wish to express my gratitude to the two anonymous referees for their valuable comments on this paper.
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