Hierarchical mixtures of experts and the EM algorithm
Neural Computation
An introduction to genetic algorithms
An introduction to genetic algorithms
Mixtures of probabilistic principal component analyzers
Neural Computation
On the identifiability of mixtures-of-experts
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive mixtures of local experts
Neural Computation
Simulated Annealing: Theory and Applications
Simulated Annealing: Theory and Applications
On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models
IEEE Transactions on Information Theory
Mixtures-of-experts of autoregressive time series: asymptotic normality and model specification
IEEE Transactions on Neural Networks
Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
On convergence rates of mixtures of polynomial experts
Neural Computation
Review: A review of novelty detection
Signal Processing
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A novel class of nonlinear models is studied based on local mixtures of autoregressive Poisson time series. The proposed model has the following construction: at any given time period, there exist a certain number of Poisson regression models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, the existence of a latent multinomial variable is assumed, whose distribution depends on the same covariates as the experts. The latent variable determines which Poisson regression is observed. This structure is a special case of the mixtures-of-experts class of models, which is considerably flexible in modelling the conditional mean function. A formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator is presented, under stationarity and nonstationarity. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, an application to a real data set is presented, in order to illustrate the ability of the proposed structure to flexibly model the conditional distribution function.