Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Neural Computation
Looking at Markov samplers through cusum path plots: a simple diagnostic idea
Statistics and Computing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
Statistics and Computing
Multivariate mixtures of normals with unknown number of components
Statistics and Computing
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Non-Gaussian factor analysis differs from ordinary factor analysis because of the distribution assumption on the factors which are modelled by univariate mixtures of Gaussians thus relaxing the classical normal hypothesis. From this point of view, the model can be thought of as a generalization of ordinary factor analysis and its estimation problem can still be solved via the maximum likelihood method. The focus of this work is to introduce, develop and explore a Bayesian analysis of the model in order to provide an answer to unresolved questions about the number of latent factors and simultaneously the number of mixture components to model each factor. The effectiveness of the proposed method is explored in a simulation study and in a real example of international exchange rates.