6 research outputs found
Modeling nonlinearities with mixtures-of-experts of time series models
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
Bayesian Nonparametric Density Autoregression with Lag Selection
We develop a Bayesian nonparametric autoregressive model applied to flexibly
estimate general transition densities exhibiting nonlinear lag dependence. Our
approach is related to Bayesian density regression using Dirichlet process
mixtures, with the Markovian likelihood defined through the conditional
distribution obtained from the mixture. This results in a Bayesian
nonparametric extension of a mixtures-of-experts model formulation. We address
computational challenges to posterior sampling that arise from the Markovian
structure in the likelihood. The base model is illustrated with synthetic data
from a classical model for population dynamics, as well as a series of waiting
times between eruptions of Old Faithful Geyser. We study inferences available
through the base model before extending the methodology to include automatic
relevance detection among a pre-specified set of lags. Inference for global and
local lag selection is explored with additional simulation studies, and the
methods are illustrated through analysis of an annual time series of pink
salmon abundance in a stream in Alaska. We further explore and compare
transition density estimation performance for alternative configurations of the
proposed model
A Nonlinear Mixture Autoregressive Model For Speaker Verification
In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification