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In this paper we consider a set of time-series that are coupled by latent fractional Gaussian processes. Specifically, we address time-series that combine idiosyncratic short-term and shared long-term features. The long-memory is modeled by fractional Gaussian processes, whereas the short-memory properties are captured by linear models of past data. The observations are nonlinear functions of the...
In the past decades, Sequential Monte Carlo (SMC) sampling has proven to be a method of choice in many applications where the dynamics of the studied system are described by nonlinear equations and/or non-Gaussian noises. In this paper, we study the application of SMC sampling to nonlinear state-space models where the state is a fractional Gaussian process. These processes are characterized by long-memory...
In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is...
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