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In this paper we give optimal constants in Talagrand's concentration inequalities for maxima of empirical processes associated to independent and eventually nonidentically distributed random variables. Our approach is based on the entropy…

Probability · Mathematics 2007-05-23 T. Klein , E. Rio

The problem of finding the expected value of a statistic of a locally stable point process in a bounded region is addressed. We propose an adaptive importance sampling for solving the problem. In our proposal, we restrict the importance…

Machine Learning · Statistics 2025-03-04 Hee-Geon Kang , Sunggon Kim

This paper considers the problem of adaptive estimation of a non-homogeneous intensity function from the observation of n independent Poisson processes having a common intensity that is randomly shifted for each observed trajectory. We show…

Statistics Theory · Mathematics 2011-05-20 Jérémie Bigot , Sébastien Gadat , Thierry Klein , Clément Marteau

A compound Poisson process whose jump measure and intensity are unknown is observed at finitely many equispaced times. We construct a purely data-driven estimator of the L\'evy density $\nu$ through the spectral approach using general…

Statistics Theory · Mathematics 2019-02-12 Alberto J. Coca

We consider the nonparametric estimation of the intensity function of a Poisson point process in a circular model from indirect observations $N_1,\ldots,N_n$. These observations emerge from hidden point process realizations with the target…

Statistics Theory · Mathematics 2019-02-19 Martin Kroll

The intensity of a Gibbs point process is usually an intractable function of the model parameters. For repulsive pairwise interaction point processes, this intensity can be expressed as the Laplace transform of some particular function.…

Methodology · Statistics 2017-12-11 Jean-François Coeurjolly , Frédéric Lavancier

We study systems of simple point processes that admit stochastic intensities. We represent these point processes as thinnings of Poisson measures and are interested in a convergence result of such systems. This result states that, if the…

Probability · Mathematics 2021-05-11 Xavier Erny

Motivated by applications in cybersecurity and epidemiology, we consider the problem of detecting an abrupt change in the intensity of a Poisson process, characterised by a jump (non transitory change) or a bump (transitory change) from…

Statistics Theory · Mathematics 2021-06-09 Magalie Fromont , Fabrice Grela , Ronan Le Guével

In this paper we study the regularity of paths in terms of properties of admissible nets. We show the right concentration inequality above the modulus of continuity. Using the approach we prove the Bernstein type inequality for the…

Probability · Mathematics 2014-05-09 Witold Bednorz

We focus on the estimation of the intensity of a Poisson process in the presence of a uniform noise. We propose a kernel-based procedure fully calibrated in theory and practice. We show that our adaptive estimator is optimal from the oracle…

Methodology · Statistics 2022-06-29 Anna Bonnet , Claire Lacour , Franck Picard , Vincent Rivoirard

In this article we obtain concentration inequalities for Poisson $U$-statistics $F_m(f,\eta)$ of order $m\ge 1$ with kernels $f$ under general assumptions on $f$ and the intensity measure $\gamma \Lambda$ of underlying Poisson point process…

Probability · Mathematics 2024-08-12 Gilles Bonnet , Anna Gusakova

Non-homogeneous Poisson processes are used in a wide range of scientific disciplines, ranging from the environmental sciences to the health sciences. Often, the central object of interest in a point process is the underlying intensity…

Methodology · Statistics 2022-02-11 Tin Lok James Ng , Andrew Zammit-Mangion

We derive transport-entropy inequalities for mixed binomial point processes, and for Poisson point processes. We show that when the finite intensity measure satisfies a Talagrand transport inequality, the law of the point process also…

Probability · Mathematics 2024-06-21 Nathael Gozlan , Ronan Herry , Giovanni Peccati

In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a $d$-dimensional domain. This method was proposed by Adams, Murray…

Statistics Theory · Mathematics 2015-03-03 Alisa Kirichenko , Harry van Zanten

The purpose of this paper is to estimate the intensity of a Poisson process $N$ by using thresholding rules. In this paper, the intensity, defined as the derivative of the mean measure of $N$ with respect to $ndx$ where $n$ is a fixed…

Statistics Theory · Mathematics 2008-01-22 Patricia Reynaud-Bouret , Vincent Rivoirard

We propose to test the homogeneity of a Poisson process observed on a finite interval. In this framework, we first provide lower bounds for the uniform separation rates in $\mathbb{L}^2$ norm over classical Besov bodies and weak Besov…

Statistics Theory · Mathematics 2009-05-08 M. Fromont , B. Laurent , P. Reynaud-Bouret

We study the nonparametric estimation of the jump density of a compound Poisson process from the discrete observation of one trajectory over $[0,T]$. We consider the microscopic regime when the sampling rate $\Delta=\Delta_T\rightarrow0$ as…

Statistics Theory · Mathematics 2012-03-15 Céline Duval

Concentration properties of functionals of general Poisson processes are studied. Using a modified $\Phi$-Sobolev inequality a recursion scheme for moments is established, which is of independent interest. This is applied to derive moment…

Probability · Mathematics 2022-03-17 Anna Gusakova , Holger Sambale , Christoph Thaele

This note aims at presenting several new theoretical results for the compound Poisson point process, which follows the work of Zhang \emph{et al.} [Insurance~Math.~Econom.~59(2014), 325-336]. The first part provides a new characterization…

Statistics Theory · Mathematics 2019-12-10 Huiming Zhang , Xiaoxu Wu

We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower dimensional projections. Our model combines the techniques…

Machine Learning · Statistics 2020-06-17 Simon Luo , Feng Zhou , Lamiae Azizi , Mahito Sugiyama
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