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Random intersection graphs are characterized by three parameters: $n$, $m$ and $p$, where $n$ is the number of vertices, $m$ is the number of objects, and $p$ is the probability that a given object is associated with a given vertex. Two…

Combinatorics · Mathematics 2016-09-07 Katarzyna Rybarczyk , Dudley Stark

We offer an alternative proof, using the Stein-Chen method, of Bollob\'{a}s' theorem concerning the distribution of the extreme degrees of a random graph. Our proof also provides a rate of convergence of the extreme degree to its asymptotic…

Combinatorics · Mathematics 2023-11-20 Yaakov Malinovsky

As an application of Stein's method for Poisson approximation, we prove rates of convergence for the tail probabilities of two scan statistics that have been suggested for detecting local signals in sequences of independent random variables…

Probability · Mathematics 2015-05-29 Xiao Fang , David Siegmund

We use the Stein-Chen method to prove new explicit inequalities for the total variation, Wasserstein and local distances between the distribution of a random diagonal sum of a Bernoulli matrix and a Poisson distribution. Approximation…

Probability · Mathematics 2024-09-04 Bero Roos

In this article, we derive Stein's method for approximating a spatial random graph by a generalised random geometric graph, which has vertices given by a finite Gibbs point process and edges based on a general connection function. Our main…

Probability · Mathematics 2024-11-06 Dominic Schuhmacher , Leoni Carla Wirth

We prove a Poisson limit theorem in the total variation distance of functionals of a general Poisson point process using the Malliavin-Stein method. Our estimates only involve first and second order difference operators and are closely…

Probability · Mathematics 2019-05-28 Jens Grygierek

With the advent of structured data in the form of social networks, genetic circuits and protein interaction networks, statistical analysis of networks has gained popularity over recent years. Stochastic block model constitutes a classical…

Statistics Theory · Mathematics 2015-05-27 Debdeep Pati , Anirban Bhattacharya

In this paper, we investigate the induced subgraphs of percolated random geometric graphs, and get some asymptotic results for the expected number of the subgraphs. Moreover, we get the Poisson approximation for the counting by Stein's…

Probability · Mathematics 2018-03-08 Anshui Li

Convergence of order $O(1/\sqrt{n})$ is obtained for the distance in total variation between the Poisson distribution and the distribution of the number of fixed size cycles in generalized random graphs with random vertex weights. The…

Probability · Mathematics 2024-05-31 Sergey G. Bobkov , Maria A. Danshina , Vladimir V. Ulyanov

A $d$-dimensional Ising model on a lattice torus is considered. As the size $n$ of the lattice tends to infinity, a Poisson approximation is given for the distribution of the number of copies in the lattice of any given local configuration,…

Probability · Mathematics 2009-11-11 David Coupier

A stochastic ordering approach is applied with Stein's method for approximation by the equilibrium distribution of a birth-death process. The usual stochastic order and the more general s-convex orders are discussed. Attention is focused on…

Probability · Mathematics 2009-12-09 Fraser Daly , Claude Lefèvre , Sergey Utev

We consider self-loops and multiple edges in the configuration model as the size of the graph tends to infinity. The interest in these random variables is due to the fact that the configuration model, conditioned on being simple, is a…

Probability · Mathematics 2017-02-06 Omer Angel , Remco van der Hofstad , Cecilia Holmgren

We establish Poisson and compound Poisson approximations for stabilizing statistics of $\beta$-mixing point processes and give explicit rates of convergence. Our findings are based on a general estimate of the total variation distance of a…

Probability · Mathematics 2023-10-24 Nicolas Chenavier , Moritz Otto

Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks…

Methodology · Statistics 2016-02-10 Shakira Suwan , Dominic S. Lee , Runze Tang , Daniel L. Sussman , Minh Tang , Carey E. Priebe

We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduces several technical difficulties for model estimation,…

Machine Learning · Statistics 2013-05-27 Christopher Aicher , Abigail Z. Jacobs , Aaron Clauset

In this paper, a new method based on probability generating functions is used to obtain multiple Stein operators for various random variables closely related to Poisson, binomial and negative binomial distributions. Also, Stein operators…

Probability · Mathematics 2016-05-10 N. S. Upadhye , V. Cekanavicius , P. Vellaisamy

Concentration inequalities for subgraph counts in random geometric graphs built over Poisson point processes are proved. The estimates give upper bounds for the probabilities $\mathbb{P}(N\geq M +r)$ and $\mathbb{P}(N\leq M - r)$ where $M$…

Probability · Mathematics 2015-04-29 Sascha Bachmann , Matthias Reitzner

We consider the limiting behavior of the count of subgraphs isomorphic to a graph $G$ with $m\geq 0$ fixed endpoints (or roots) in the random-connection model, as the intensity $\lambda$ of the underlying Poisson point process tends to…

Probability · Mathematics 2025-11-11 Qingwei Liu , Nicolas Privault

Compound Poisson distributions and signed compound Poisson measures are used for approximation of the Markov binomial distribution. The upper and lower bound estimates are obtained for the total variation, local and Wasserstein norms. In a…

Statistics Theory · Mathematics 2010-11-29 V. Čekanavičius , P. Vellaisamy

We provide a general result for bounding the difference between point probabilities of integer supported distributions and the translated Poisson distribution, a convenient alternative to the discretized normal. We illustrate our theorem in…

Probability · Mathematics 2017-12-05 A. D. Barbour , Adrian Röllin , Nathan Ross