Mixture of regression models with latent variables and sparse coefficient parameters.
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Author(s)
Ng, Shu-Kay
McLachlan, Geoffrey J
Griffith University Author(s)
Year published
2014
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Show full item recordAbstract
Mixture models have been widely used in marketing research and epidemiology
to capture heterogeneity in endogenous latent variables among individuals. However, when
collinearity between endogenous latent variables at the component level is present, some componentspecific
path coefficients will be zero. In this paper, a systematic computational algorithm is
developed to identify parameters that need to be constrained to be zero and to address other
issues including the initialization procedure, the provision of standard errors of estimates, and
the method to determine the number of components. The proposed algorithm is ...
View more >Mixture models have been widely used in marketing research and epidemiology to capture heterogeneity in endogenous latent variables among individuals. However, when collinearity between endogenous latent variables at the component level is present, some componentspecific path coefficients will be zero. In this paper, a systematic computational algorithm is developed to identify parameters that need to be constrained to be zero and to address other issues including the initialization procedure, the provision of standard errors of estimates, and the method to determine the number of components. The proposed algorithm is illustrated using simulated data and a real data set concerning emotional behaviour of preschool children.
View less >
View more >Mixture models have been widely used in marketing research and epidemiology to capture heterogeneity in endogenous latent variables among individuals. However, when collinearity between endogenous latent variables at the component level is present, some componentspecific path coefficients will be zero. In this paper, a systematic computational algorithm is developed to identify parameters that need to be constrained to be zero and to address other issues including the initialization procedure, the provision of standard errors of estimates, and the method to determine the number of components. The proposed algorithm is illustrated using simulated data and a real data set concerning emotional behaviour of preschool children.
View less >
Conference Title
Proceedings of COMPSTAT 2014, 21st International Conference on Computational Statistics.
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Subject
Medical and Health Sciences not elsewhere classified