M. De Domenico

Fast detection of nonlinearity and nonstationarity in short and noisy time series

We introduce a statistical method to detect nonlinearity and nonstationarity in time series, that works even for short sequences and in the presence of noise. The method has a discrimination power similar to that of the most advanced estimators on the market, yet it depends only on one parameter, is easier to implement and faster. Applications to real data sets reject the null hypothesis of an underlying stationary linear stochastic process with a higher confidence interval than the best known nonlinear discriminators up to date.

Multiscale autocorrelation function: a new approach to anisotropy studies

We present a novel catalog-independent method, based on a scale dependent approach, to detect anisotropy signatures in the arrival direction distribution of the ultra highest energy cosmic rays (UHECR). The method provides a good discrimination power for both large and small data sets, even in presence of strong contaminating isotropic background. We present some applications to simulated data sets of events corresponding to plausible scenarios for charged particles detected in the last decades by world-wide surface detector-based observatories.

Scaling and universality in river flow dynamics

We investigate flow dynamics in rivers characterized by basin areas and daily mean discharge spanning different orders of magnitude. We show that the delayed increments evaluated at time scales ranging from days to months can be opportunely rescaled to the same non-Gaussian probability density function. Such a scaling breaks up above a certain critical horizon, where a behavior typical of thermodynamic systems at the critical point emerges. We finally show that both the scaling behavior and the break-up of the scaling are universal features of river flow dynamics. Copyright c EPLA, 2011

Anisotropy studies with multiscale autocorrelation function

We present a novel method, based on a multiscale approach, for detecting anisotropy signatures in the arrival direction distribution of the highest energy cosmic rays. This method is catalog independent, i.e. it does not depend on the choice of a particular catalog of candidate sources, and it provides a good discrimination power even in presence of contaminating isotropic background. We present applications to simulated data sets of events corresponding to plausible scenarios for events detected, in the last decades, by world-wide surface detector-based observatories for charged particles.

A MULTISCALE AUTOCORRELATION FUNCTION FOR ANISOTROPY STUDIES

In recent years many procedures have been proposed to check the anisotropy of a dataset. We present a new simple procedure, based on a scale dependent approach, to detect anisotropy signatures in a given distribution with particular attention to small dataset. The method provides a good discrimination power for both large and small datasets, even in presence of strong contaminating isotropic background. We present some applications to simulated datasets of events to investigate statistical features of the method and present and inspect its behavior under both the null or the alternative hypothesis.