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We study a new family of random variables, that each arise as the distribution of the maximum or minimum of a random number $N$ of i.i.d.~random variables $X_1,X_2,\ldots,X_N$, each distributed as a variable $X$ with support on $[0,1]$. The…

Statistics Theory · Mathematics 2014-03-07 Jie Hao , Anant Godbole

Recently, considerable attention has been devoted to the prediction problems arising from heterogeneous information networks. In this paper, we present a new prediction task, Neighbor Distribution Prediction (NDP), which aims at predicting…

Social and Information Networks · Computer Science 2015-09-29 Yuchi Ma , Ning Yang , Chuan Li , Lei Zhang , Philip S. Yu

This paper introduces a new distribution to improve tail risk modeling. Based on the classical normal distribution, we define a new distribution by a series of heat equations. Then, we use market data to verify our model.

Statistics Theory · Mathematics 2013-04-08 Xiaolin Gong , Shuzhen Yang

This article introduces a non-parametric information-theoretic approach to inference about the tail of a continuous or a discrete distribution. Leveraging a new concept named tail profile -- a set of information-theoretic quantities…

Applications · Statistics 2025-03-19 Jialin Zhang , Zhiyi Zhang

We consider the question of learning the natural parameters of a $k$ parameter minimal exponential family from i.i.d. samples in a computationally and statistically efficient manner. We focus on the setting where the support as well as the…

Machine Learning · Computer Science 2021-11-01 Abhin Shah , Devavrat Shah , Gregory W. Wornell

Polynomials are common algebraic structures, which are often used to approximate functions including probability distributions. This paper proposes to directly define polynomial distributions in order to describe stochastic properties of…

Information Theory · Computer Science 2022-12-12 Yue Yu , Pavel Loskot

Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a non-parametric Bayesian approach to estimate the intensity…

Statistics Theory · Mathematics 2020-05-21 Shota Gugushvili , Ester Mariucci , Frank van der Meulen

The paper presents a novel asymptotic distribution for a mle when the log--likelihood is strictly concave in the parameter for all data points; for example, the exponential family. The new asymptotic distribution can be seen as a refinement…

Statistics Theory · Mathematics 2021-06-15 Stephen G Walker

Matrix-form Poisson probability distributions were recently introduced as one matrix generalization of Panjer distributions. We show in this paper that under the constraint that their representation is to be nonnegative, they have a…

Probability · Mathematics 2012-11-15 Sophie Hautphenne , Guy Latouche , Giang Nguyen

In this second part of our survey on the social and natural distributions, we investigate some models, which intend to explain the statistical regularity of the natural and social distributions. There is a large variety of models and in…

Physics and Society · Physics 2016-07-05 L. Benguigui , M. Marinov

Starting with just the assumption of uniformly distributed orbital orientations, we derive expressions for the distributions of the Keplerian orbital elements as functions of arbitrary distributions of eccentricity and semi-major axis. We…

Instrumentation and Methods for Astrophysics · Physics 2011-01-24 Dmitry Savransky , Eric Cady , N. Jeremy Kasdin

A Poisson Binomial distribution over $n$ variables is the distribution of the sum of $n$ independent Bernoullis. We provide a sample near-optimal algorithm for testing whether a distribution $P$ supported on $\{0,...,n\}$ to which we have…

Data Structures and Algorithms · Computer Science 2014-10-15 Jayadev Acharya , Constantinos Daskalakis

A method for estimating the Shannon differential entropy of multidimensional random variables using independent samples is described. The method is based on decomposing the distribution into a product of the marginal distributions and the…

Statistical Mechanics · Physics 2020-04-22 Gil Ariel , Yoram Louzoun

We introduce the discrete distribution of a Wiener process range. Rather than finding some basic distributional properties including hazard rate function, moments, Stress-strength parameter and order statistics of this distribution, this…

Statistics Theory · Mathematics 2017-10-24 Mohamed Abd Allah El-Hadidy

We consider a type of nonnormal approximation of infinitely divisible distributions that incorporates compound Poisson, Gamma, and normal distributions. The approximation relies on achieving higher orders of cumulant matching, to obtain…

Probability · Mathematics 2013-04-24 Zhiyi Chi

We study a new kind of proximity graphs called proportional-edge proximity catch digraphs (PCDs)in a randomized setting. PCDs are a special kind of random catch digraphs that have been developed recently and have applications in statistical…

Combinatorics · Mathematics 2010-03-30 Elvan Ceyhan

An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness has received much attention in the model-based clustering literature recently, we investigate the use of a…

Methodology · Statistics 2015-06-15 Utkarsh J. Dang , Ryan P. Browne , Paul D. McNicholas

The concept of scattered polynomials is generalized to those of exceptional scattered sequences which are shown to be the natural algebraic counterpart of $\mathbb{F}_{q^n}$-linear MRD codes. The first infinite family in the first…

Combinatorics · Mathematics 2022-11-22 Daniele Bartoli , Giuseppe Marino , Alessandro Neri , Lara Vicino

Inspired by R. Speicher's multidimensional free central limit theorem and semicircle families, we prove an infinite dimensional compound Poisson limit theorem in free probability, and define infinite dimensional compound free Poisson…

Operator Algebras · Mathematics 2017-12-19 Guimei An , Mingchu Gao

Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point…

Machine Learning · Computer Science 2024-10-14 Yuchen Ma , Valentyn Melnychuk , Jonas Schweisthal , Stefan Feuerriegel
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