38,579 research outputs found
Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG
This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings
Invaded cluster algorithm for Potts models
The invaded cluster algorithm, a new method for simulating phase transitions,
is described in detail. Theoretical, albeit nonrigorous, justification of the
method is presented and the algorithm is applied to Potts models in two and
three dimensions. The algorithm is shown to be useful for both first-order and
continuous transitions and evidently provides an efficient way to distinguish
between these possibilities. The dynamic properties of the invaded cluster
algorithm are studied. Numerical evidence suggests that the algorithm has no
critical slowing for Ising models.Comment: 39 pages, revtex, 15 figures available on request from
[email protected], to appear in Phys. Rev.
Scaling Analysis of Fluctuating Strength Function
We propose a new method to analyze fluctuations in the strength function
phenomena in highly excited nuclei. Extending the method of multifractal
analysis to the cases where the strength fluctuations do not obey power scaling
laws, we introduce a new measure of fluctuation, called the local scaling
dimension, which characterizes scaling behavior of the strength fluctuation as
a function of energy bin width subdividing the strength function. We discuss
properties of the new measure by applying it to a model system which simulates
the doorway damping mechanism of giant resonances. It is found that the local
scaling dimension characterizes well fluctuations and their energy scales of
fine structures in the strength function associated with the damped collective
motions.Comment: 22 pages with 9 figures; submitted to Phys. Rev.
Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series
Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be extended in multiple ways to time series analysis
Statistical properties and economic implications of Jump-Diffusion Processes with Shot-Noise effects
This paper analyzes the Shot-Noise Jump-Diffusion model of Altmann, Schmidt and Stute (2008), which
introduces a new situation where the effects of the arrival of rare, shocking information to the financial
markets may fade away in the long run. We analyze several economic implications of the model,
providing an analytical expression for the process distribution. We also prove that certain specifications
of this model can provide negative serial persistence. Additionally, we find that the degree of serial
autocorrelation is related to the arrival and magnitude of abnormal information. Finally, a GMM
framework is proposed to estimate the model parameters
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