We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by ...

Gaussian Mixture Models (GMM) are an effective representation of resource uncertainty in power systems planning, as they can be tractably incorporated within stochastic optimization models. However, ...

The establishment of data association is a vital component to ensure the real-time and precision of object tracking. In view of the deficiency of real-time and precision of traditional data ...

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Materials and methods Methods for learning Gaussian Graphic Models from independent observations A Gaussian Graphic Model (GGM) is a statistical model that represents properties of marginal and ...