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D Kugiumtzis

Publications and source records attributed to D Kugiumtzis.

5 recordsLinked to original sources

Statistical analysis of the extreme values of stress time series from the Portevin-Le Châtelier effect.

In an effort to understand the deterministic vs stochastic character of the Portevin-Le Châtelier (PLC) phenomenon, we investigate the structure of the underlying mechanism that generates the stick-slip patterns of stress over time. The stress time series is reduced to a series of successive pairs of minimum and maximum values representing the stick-slip patterns and a statistical analysis by means of hypothesis testing is applied to it. The null hypothesis of least deterministic structure is that the time series of extreme values is a bounded random walk of alternating direction (BRWAD); that is, besides the constraint of succession of minima to maxima bounded at a predefined range there are no other correlations in the data. To implement the test we use surrogate data generated by a model consistent with a BRWAD type process, which also uses the statistics of the original data to best mimic them. The proposed hypothesis testing is found to perform properly on simulated data from stochastic and deterministic systems. For the PLC time series, the null hypothesis is rejected at a high level of confidence giving evidence for some deterministic structure in the succession of the extreme stress values. This result allows for further statistical analysis including also the time aspect of the stick-slip patterns.

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Statically transformed autoregressive process and surrogate data test for nonlinearity.

The key feature for the successful implementation of the surrogate data test for nonlinearity on a scalar time series is the generation of surrogate data that represent exactly the null hypothesis (statically transformed normal stochastic process), i.e., they possess the sample autocorrelation and amplitude distribution of the given data. A conceptual approach and algorithm for the generation of surrogate data is proposed, called the statically transformed autoregressive process (STAP). It identifies a normal autoregressive process and a monotonic static transform, so that the transformed realizations of this process fulfill exactly both conditions and do not suffer from bias in autocorrelation as the surrogate data generated by other algorithms. The appropriateness of STAP is demonstrated with simulated and real world data.

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Surrogate data test for nonlinearity including nonmonotonic transforms

It is shown that monotonicity of the transform in the surrogate data test, which diminishes the applicability of the test, is not necessary and concerns only the prominent algorithm of amplitude adjusted Fourier transform (AAFT) for surrogate data generation. The failure of AAFT under nonmonotonicity is explained and a modified algorithm appropriate for nonmonotonic transforms, called corrected AAFT (CAAFT), is proposed. The superiority of CAAFT over AAFT is demonstrated with simulated and real data and compared also to the iterated AAFT algorithm.

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Test your surrogate data before you test for nonlinearity.

The schemes for the generation of surrogate data in order to test the null hypothesis of linear stochastic process undergoing nonlinear static transform are investigated as to their consistency in representing the null hypothesis. In particular, we pinpoint some important caveats of the prominent algorithm of amplitude adjusted Fourier transform surrogates (AAFT) and compare it to the iterated AAFT, which is more consistent in representing the null hypothesis. It turns out that in many applications with real data the inferences of nonlinearity after marginal rejection of the null hypothesis were premature and have to be reinvestigated taking into account the inaccuracies in the AAFT algorithm, mainly concerning the mismatching of the linear correlations. In order to deal with such inaccuracies, we propose the use of linear together with nonlinear polynomials as discriminating statistics. The application of this setup to some well-known real data sets cautions against the use of the AAFT algorithm.

Journal Article↗

Procedure for estimating the correlation dimension of optokinetic nystagmus signals.

In this study, optokinetic nystagmus (OKN) is hypothesized to be controlled by a low-dimensional deterministic and possibly chaotic generator. A procedure for quantifying the presumably low-dimensional structure of the OKN signal, based on the Singular Spectrum Approach and the Grassberger--Procaccia algorithm for estimating the correlation dimension, v, is described. The procedure developed showed robustness against noise. Applying this method to OKN signals from 10 healthy subjects and 10 patients suffering from vertigo showed a statistically significant lower mean v value for the patients.

Adult↗