Evidence of Deterministic Components in the Apparent Randomness of GRBs: Clues of a Chaotic Dynamic

Prompt γ-ray emissions from gamma-ray bursts (GRBs) exhibit a vast range of extremely complex temporal structures with a typical variability time-scale significantly short – as fast as milliseconds. This work aims to investigate the apparent randomness of the GRB time profiles making extensive use of nonlinear techniques combining the advanced spectral method of the Singular Spectrum Analysis (SSA) with the classical tools provided by the Chaos Theory. Despite their morphological complexity, we detect evidence of a non stochastic short-term variability during the overall burst duration – seemingly consistent with a chaotic behavior. The phase space portrait of such variability shows the existence of a well-defined strange attractor underlying the erratic prompt emission structures. This scenario can shed new light on the ultra-relativistic processes believed to take place in GRB explosions and usually associated with the birth of a fast-spinning magnetar or accretion of matter onto a newly formed black hole

Current situation on data exchange in agriculture in the EU27 & Switzerland

AgriXchange network for data exchange in agriculture -hankkeen julkaisuvokKV

The quest for nonlinearity in time series

In this chapter, we review the problem of testing for nonlinearity in time series. First, we discuss the definition and the properties of linear processes and the implications that such properties have on the operational strand. Then, we present and review a tentative classification of the various tests that can be found both in the time series and in the nonlinear dynamics literature. Two main factors contributed to the production of a plethora of alternatives for assessing nonlinearity in time series: the first factor is the intrinsic asymmetry between the linear and the nonlinear realm. In fact, there can be departures from linearity in various directions as nonlinear phenomena possess a virtually infinite richness of features. Among such features we can mention irreversibility, nonuniform predictability, noise amplification/ suppression, phase synchronization, noise-induced phenomena, sensitivity to initial conditions, and so on. The second factor is the multidisciplinary nature of the problem. Indeed, the problem of characterizing the various aspects of nonlinear processes is shared among different disciplines, such as Statistics, Econometrics, Nonlinear Dynamics, Biology, and Engineering. The review is by no means exhaustive and reflects the personal inclinations of the autho

tseriesEntropy: R package for Entropy based tests and analysis in time series

R package for Entropy based tests and analysis in time serie

Introduction to the theme issue: The skew-normal and related distributions

This theme issue on the skew Normal and related distributions is motivated by the workshop held on November 6th, 2017 in memory of Antonella Capitanio, one year after her premature loss. The issue contains the transcript of the conversation between Angela Montanari, Adelchi Azzalini and Narayanaswamy Balakrishnan regarding their scientific collaboration with Antonella. Moreover, the last unpublished work of Antonella Capitanio on mixtures of skew normal distributions is reproduced here with the kind permission of her family. We also take the opportunity to re-present the seminal 1986 Azzalini paper, together with corrections and comments from the author. The last contribution, by Howell Tong and Dong Li, concerns the interesting relationship between skew symmetric distributions and threshold autoregressive models

The wild bootstrap for multilevel models

In this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modied version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the nite size performances of the schemes through a Monte Carlo study. The results show that for big sample sizes it always pays o to adopt an agnostic approach as the wild bootstrap outperforms other techniques