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We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Tim...
We study chaotic orbits of conservative low--dimensional maps and present numerical results showing that the probability density functions (pdfs) of the sum of $N$ iterates in the large $N$ limit exhi...
The given paper is natural continuation of our previous paper arXiv:1011.4173 We have illustrated earlier, that in the classical Hamilton mechanics for an overwhelming majority of real chaotic macrosc...
Small particles transported by a fluid medium do not necessarily have to follow the flow. We show that for a wide class of time-periodic incompressible flows inertial particles have a tendency to spon...
In this paper we consider stable marriage problem under the Monte Carlo simulations. We investigate how correlation in lists of preferrences can affect simulation results such as: relaxation time, t...
This paper is concerned with inference based on the mean function of a functional time series, which is defined as a collection of curves obtained by splitting a continuous time record, e.g. into da...
By means of the shift operators we introduce a new periodicity concept on time scales. This new approach will enable researchers to investigate periodicity notion on a large class of time scales whose...
This paper presents an approach to modeling progressive event-history data when the overall objective is prediction based on time-dependent covariates.This approach does not model the hazard function ...
While modern optics is largely a physics of harmonic oscillators and two-by-two matrices, it is possible to learn about some hidden properties of the two-by-two matrix from optical systems.
Understanding of normal and pathological brain function requires the identification and localization of functional connections between specialized regions. The availability of high time resolution sig...

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