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Motivated by certain problems of statistical physics we consider a stationary stochastic process in which deterministic evolution is interrupted at random times by upward jumps of a fixed size. If the evolution consists of linear decay, the…

Statistical Mechanics · Physics 2009-10-31 O. Deloubriere , H. J. Hilhorst

Marked point process data arise when events occur in a space with event-level marks. We study clustering of replicated marked Poisson point processes and introduce Dirichlet process mixtures of marked Poisson point processes, a Bayesian…

Methodology · Statistics 2026-05-12 Minsung Choi , Seonghyun Jeong

This paper provides the basis for new methods of inference for max-stable processes \xi\ on general spaces that admit a certain incremental representation, which, in important cases, has a much simpler structure than the max-stable process…

Probability · Mathematics 2012-09-12 Sebastian Engelke , Alexander Malinowski , Marco Oesting , Martin Schlather

This paper deals with Poisson processes on an arbitrary measurable space. Using a direct approach, we derive formulae for moments and cumulants of a vector of multiple Wiener-It\^o integrals with respect to the compensated Poisson process.…

Probability · Mathematics 2014-07-08 Guenter Last , Mathew D. Penrose , Matthias Schulte , Christoph Thaele

We develop nonparametric Bayesian modelling approaches for Poisson processes, using weighted combinations of structured beta densities to represent the point process intensity function. For a regular spatial domain, such as the unit square,…

Methodology · Statistics 2021-06-10 Chunyi Zhao , Athanasios Kottas

This paper deals with inference in a class of stable but nearly-unstable processes. Autoregressive processes are considered, in which the bridge between stability and instability is expressed by a time-varying companion matrix $A_{n}$ with…

Statistics Theory · Mathematics 2023-05-18 Marie Badreau , Frédéric Proïa

In this article, we consider $\beta$-ensembles, i.e. collections of particles with random positions on the real line having joint distribution $$\frac{1}{Z_N(\beta)}|\Delta(\lambda)|^\beta e^{- \frac{N\beta}{4}\sum_{i=1}^N\lambda_i^2}d…

Probability · Mathematics 2015-06-25 Florent Benaych-Georges , Sandrine Péché

We study the largest gaps between successive zeros of a smooth stationary Gaussian process. Our main result is that, if correlations decay at least polynomially, then after suitable rescaling of the locations and sizes of the largest gaps…

Probability · Mathematics 2026-05-22 Renjie Feng , Stephen Muirhead

We consider stopping criteria that balance algebraic and discretization errors for the conjugate gradient algorithm applied to high-order finite element discretizations of Poisson problems. Firstly, we introduce a new stopping criterion…

Numerical Analysis · Mathematics 2024-08-06 Yichen Guo , Eric de Sturler , Tim Warburton

Let $W_i,i\in{\mathbb{N}}$, be independent copies of a zero-mean Gaussian process $\{W(t),t\in{\mathbb{R}}^d\}$ with stationary increments and variance $\sigma^2(t)$. Independently of $W_i$, let $\sum_{i=1}^{\infty}\delta_{U_i}$ be a…

Probability · Mathematics 2009-09-25 Zakhar Kabluchko , Martin Schlather , Laurens de Haan

The multivariate extremal index function relates the asymptotic distribution of the vector of pointwise maxima of a multivariate stationary sequence to that of the independent sequence from the same stationary distribution. It also measures…

Applications · Statistics 2008-11-14 Christian Y. Robert

Consider a stationary Poisson process $\eta$ in the $d$-dimensional Euclidean or hyperbolic space and construct a random graph with vertex set $\eta$ as follows. First, each point $x\in\eta$ is connected by an edge to its nearest neighbour,…

Probability · Mathematics 2024-11-04 Holger Sambale , Christoph Thäle , Tara Trauthwein

This work is devoted to the problem of estimation of the localization of Poisson source. The observations are inhomogeneous Poisson processes registered by the $k\geq 3$ detectors on the plane. We study the behavior of the Bayes estimators…

Statistics Theory · Mathematics 2018-06-20 O. V. Chernoyarov , S. Dachian , Yu. A. Kutoyants

A measure of primal importance for capturing the serial dependence of a stationary time series at extreme levels is provided by the limiting cluster size distribution. New estimators based on a blocks declustering scheme are proposed and…

Statistics Theory · Mathematics 2020-11-11 Axel Bücher , Tobias Jennessen

We consider parameter estimation of stochastic differential equations driven by a Wiener process and a compound Poisson process as small noises. The goal is to give a threshold-type quasi-likelihood estimator and show its consistency and…

Statistics Theory · Mathematics 2023-12-20 Mitsuki Kobayashi , Yasutaka Shimizu

In this article, we study the dynamics of a nonlinear system governed by an ordinary differential equation under the combined influence of fast periodic sampling with period $\delta$ and small jump noise of size $\varepsilon, 0<…

Probability · Mathematics 2024-11-28 Shivam Singh Dhama

Commonalities and differences in correlation analysis in terms of phase space, conditioning and uncorrelatedness are discussed. The Poisson process is not generally appropriate as reference distribution for normalisation and cumulants, so…

High Energy Physics - Experiment · Physics 2007-05-23 H. C. Eggers

We show that \emph{stochastic bursting} is observed in a ring of unidirectional delay-coupled noisy excitable systems, thanks to the combinational action of time-delayed coupling and noise. Under the approximation of timescale separation,…

Disordered Systems and Neural Networks · Physics 2019-05-01 Chunming Zheng , Arkady Pikovsky

We consider the persistence probability, the occupation-time distribution and the distribution of the number of zero crossings for discrete or (equivalently) discretely sampled Gaussian Stationary Processes (GSPs) of zero mean. We first…

Statistical Mechanics · Physics 2009-11-10 George M. C. A. Ehrhardt , Satya N. Majumdar , Alan J. Bray

We present a machine learning model for the analysis of randomly generated discrete signals, modeled as the points of an inhomogeneous, compound Poisson point process. Like the wavelet scattering transform introduced by Mallat, our…

Statistics Theory · Mathematics 2021-10-12 Michael Perlmutter , Jieqian He , Matthew Hirn