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Schlagworte
- Mixture Models (2)
- Geometric Ergodicity (1)
- Identifiability (1)
- Markov Chain (1)
- Neural networks (1)
- Nonparametric AR-ARCH (1)
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A note on the identifiability of the conditional expectation for the mixtures of neural networks
(2007)
- We consider a generalized mixture of nonlinear AR models, a hidden Markov model for which the autoregressive functions are single layer feedforward neural networks. The non trivial problem of identifiability, which is usually postulated for hidden Markov models, is addressed here.
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On Geometric Ergodicity of CHARME Models
(2007)
- In this paper we consider a CHARME Model, a class of generalized mixture of nonlinear nonparametric AR-ARCH time series. We apply the theory of Markov models to derive asymptotic stability of this model. Indeed, the goal is to provide some sets of conditions under which our model is geometric ergodic and therefore satisfies some mixing conditions. This result can be considered as the basis toward an asymptotic theory for our model.
