期刊名称：International Journal of Mathematics and Mathematical Sciences
印刷版ISSN：0161-1712
电子版ISSN：1687-0425
出版年度：2006
卷号：2006
DOI：10.1155/IJMMS/2006/19423
出版社：Hindawi Publishing Corporation
摘要：We discuss a class of nonlinear models based on
mixtures-of-experts of regressions of exponential family time
series models, where the covariates include functions of lags of
the dependent variable as well as external covariates. The
discussion covers results on model identifiability, stochastic
stability, parameter estimation via maximum likelihood estimation,
and model selection via standard information criteria.
Applications using real and simulated data are presented to
illustrate how mixtures-of-experts of time series models can be
employed both for data description, where the usual mixture
structure based on an unobserved latent variable may be
particularly important, as well as for prediction, where only the
mixtures-of-experts flexibility matters.
