@InProceedings{ip-Urteaga2016a,
  author    = {I{\~n}igo Urteaga and M\'{o}nica F. Bugallo and Petar M Djuri\'{c}},
  title     = {{Sequential Monte Carlo sampling for correlated latent long-memory time-series}},
  booktitle = {2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2016},
  pages     = {6580-6584},
  month     = {March},
  abstract  = {In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional Gaussian processes have interesting properties including long-memory, self-similarity and scale-invariance, and thus, are of interest for building models in finance and econometrics. We propose sequential Monte Carlo (SMC) methods for inference of the latent processes where each method is based on different assumptions about the parameters of the state-space model. The methods are extensively evaluated via simulations of the popular stochastic volatility model.},
  doi       = {10.1109/ICASSP.2016.7472945},
  keywords  = {Gaussian noise;Monte Carlo methods;particle filtering (numerical methods);sequential estimation;state-space methods;stochastic processes;time series;correlated latent long-memory time-series;correlated mixtures;fractional Gaussian processes;nonlinear functions;sequential Monte Carlo sampling;state-space models;stochastic volatility model;white Gaussian noises;Biological system modeling;Econometrics;Gaussian noise;Gaussian processes;Monte Carlo methods;State-space methods;Sequential Monte Carlo;operator fractional Gaussian process;particle filtering;state-space models;time-series},
  owner     = {iurteaga},
  timestamp = {2016-04-14},
}

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Bugallo and Petar M Djuri\\'{c}},\n title = {{Sequential Monte Carlo sampling for correlated latent long-memory time-series}},\n booktitle = {2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\n year = {2016},\n pages = {6580-6584},\n month = {March},\n abstract = {In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional Gaussian processes have interesting properties including long-memory, self-similarity and scale-invariance, and thus, are of interest for building models in finance and econometrics. We propose sequential Monte Carlo (SMC) methods for inference of the latent processes where each method is based on different assumptions about the parameters of the state-space model. 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