相关论文: Nonlinear analysis of EAS clusters

The paper describes lateral density distributions of electrons and muons of cosmic ray extensive air showers (EAS) in the energy regime of the KASCADE experiment. Potential EAS observables are extracted while some suitable lateral…

Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by…

Complex systems are often non-stationary, typical indicators are continuously changing statistical properties of time series. In particular, the correlations between different time series fluctuate. Models that describe the multivariate…

Stage-IV galaxy surveys will measure correlations at small cosmological scales with high signal-to-noise ratio. One of the main challenges of extracting information from small scales is devising accurate models, as well as characterizing…

The performance of a number of different measures of nonlinearity in a time series is compared numerically. Their power to distinguish noisy chaotic data from linear stochastic surrogates is determined by Monte Carlo simulation for a number…

Using classical electrodynamics we determine the angular dependence of the light intensities radiated in second and third harmonic generation by spherical metal clusters. Forward and backward scattering is analyzed in detail. Also resonance…

Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the…

Air shower simulation programs are essential tools for the analysis of data from cosmic ray experiments and for planning the layout of new detectors. They are used to estimate the energy and mass of the primary particle. Unfortunately the…

The purpose of this paper is twofold: first we want to introduce a new type of hadronic interaction model (NEXUS), which has a much more solid theoretical basis as, for example, presently used models like QGSJET and VENUS, and ensures…

The radio detection technique of cosmic ray air showers has gained renewed interest in the last two decades. While the radio experiments are very cost-effective to deploy, the Monte-Carlo simulations required to analyse the data are…

We consider the problem of analyzing the heterogeneity of clustering distributions for multiple groups of observed data, each of which is indexed by a covariate value, and inferring global clusters arising from observations aggregated over…

In this paper, a novel method to perform model-based clustering of time series is proposed. The procedure relies on two iterative steps: (i) K global forecasting models are fitted via pooling by considering the series pertaining to each…

We propose a methodology to explore and measure the pairwise correlations that exist between variables in a dataset. The methodology leverages copulas for encoding dependence between two variables, state-of-the-art optimal transport for…

The analysis of high-energy air shower data allows one to study the proton-air cross section at energies beyond the reach of fixed target and collider experiments. The mean depth of the first interaction point and its fluctuations are a…

In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface; the sample may be contaminated with outliers, which are modeled as points…

This paper proposes a nonparametric Bayesian framework called VariScan for simultaneous clustering, variable selection, and prediction in high-throughput regression settings. Poisson-Dirichlet processes are utilized to detect…

We discuss a new Monte Carlo algorithm for the simulation of complex fluids. This algorithm employs geometric operations to identify clusters of particles that can be moved in a rejection-free way. It is demonstrated that this geometric…

A SAS macro, GEECORR, has been developed for the analysis of correlated binary data based on the Prentice (1988) estimating equations method that extends the Liang and Zeger (1986) generalized estimating equations (GEE) method to include…

The paper is focused on the forecasting method for time series groups with the use of algorithms for cluster analysis. $K$-means algorithm is suggested to be a basic one for clustering. The coordinates of the centers of clusters have been…

We study general nonlinear models for time series networks of integer and continuous valued data. The vector of high dimensional responses, measured on the nodes of a known network, is regressed non-linearly on its lagged value and on…