Multivariate time series modeling and classification via hierarchical VAR mixtures
Computational Statistics & Data Analysis
Modelling nonlinear count time series with local mixtures of Poisson autoregressions
Computational Statistics & Data Analysis
Gating echo state neural networks for time series forecasting
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
On convergence rates of mixtures of polynomial experts
Neural Computation
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We consider a class of nonlinear models based on mixtures of local autoregressive time series. At any given time point, we have a certain number of linear models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, we assume the existence of a latent multinomial variable, whose distribution depends on the same covariates as the experts, that determines which linear process is observed. This structure, denoted as mixture-of-experts (ME), is considerably flexible in modeling the conditional mean function, as shown by Jiang and Tanner. We present a formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator (MLE), under stationarity and nonstationarity, and under correct model specification and model misspecification. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, we present applications to simulated and real data sets, to illustrate the ability of the proposed structure to model not only the conditional mean, but also the whole conditional density.