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The family of q-Gaussian and q-exponential probability densities fit the statistical behavior of diverse complex self-similar non-equilibrium systems. These distributions, independently of the underlying dynamics, can rigorously be obtained…

Statistical Mechanics · Physics 2015-05-19 Adrian A. Budini

Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the…

Methodology · Statistics 2025-11-12 Lorenzo Ghilotti , Federico Camerlenghi , Tommaso Rigon

In this paper, we discuss computational aspects to obtain accurate inferences for the parameters of the generalized gamma (GG) distribution. Usually, the solution of the maximum likelihood estimators (MLE) for the GG distribution have no…

Computation · Statistics 2017-07-26 Jorge Alberto Achcar , Pedro Luiz Ramos , Edson Zangiacomi Martinez

In this paper, we have studied the stochastic comparisons of the highest and lowest order statistics of exponentiated Gumble type-II distribution with three parameters. We have compared both the statistics by using three different…

Statistics Theory · Mathematics 2019-04-19 Surojit Biswas , Nitin Gupta

We propose a new method for the calculation of the statistical properties, as e.g. the entropy, of unknown generators of symbolic sequences. The probability distribution $p(k)$ of the elements $k$ of a population can be approximated by the…

chao-dyn · Physics 2009-10-28 Thorsten Pöschel , Werner Ebeling , Helge Rosé

This paper introduces a general class of hierarchical nonparametric prior distributions. The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures. The…

Methodology · Statistics 2018-03-16 Federico Bassetti , Roberto Casarin , Luca Rossini

We consider a Bayesian approach to variable selection in the presence of high dimensional covariates based on a hierarchical model that places prior distributions on the regression coefficients as well as on the model space. We adopt the…

Statistics Theory · Mathematics 2014-07-28 Naveen Naidu Narisetty , Xuming He

We generalize the generalized-squeezing problem to include fractional values of the squeezing order $n$. This approach allows us to determine the locations of critical points at which qualitative changes in behaviour occur and accurately…

Quantum Physics · Physics 2026-01-23 Sahel Ashhab

The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At face value however, distributional properties are not…

Programming Languages · Computer Science 2019-03-27 Zenna Tavares , Xin Zhang , Edgar Minaysan , Javier Burroni , Rajesh Ranganath , Armando Solar Lezama

For any $d \geq 1$, random $\mathbb{Z}^d$ shifts of finite type (SFTs) were defined in previous work of the authors. For a parameter $\alpha \in [0,1]$, an alphabet $\mathcal{A}$, and a scale $n \in \mathbb{N}$, one obtains a distribution…

Dynamical Systems · Mathematics 2017-05-02 Kevin McGoff , Ronnie Pavlov

Let $T$ be a tree with induced partial order $\preceq$. We investigate centered Gaussian processes $X=(X_t)_{t\in T}$ represented as $$ X_t=\sigma(t)\sum_{v \preceq t}\alpha(v)\xi_v $$ for given weight functions $\alpha$ and $\sigma$ on $T$…

Probability · Mathematics 2012-12-04 Mikhail Lifshits , Werner Linde

For a bivariate random vector (X,Y), symmetry conditions are presented that yield stochastic orderings among |X|, |Y|, |max(X,Y)|, and | min(X, Y)|. Partial extensions of these results for multivariate random vectors (X1,...,Xn) are also…

Statistics Theory · Mathematics 2012-06-22 Yindeng Jiang , Michael D. Perlman

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the…

Machine Learning · Computer Science 2016-06-13 Bryan He , Christopher De Sa , Ioannis Mitliagkas , Christopher Ré

We consider a sequence of random Hamiltonians $H_n(h,\sigma)=\sum^n_{i=1}h_i(\sigma_i-m)$, and study the asymptotic ($n\to \infty$) distribution of the energy levels $(H_n(h,\sigma))_{\sigma\in \{-1,1\}^n}$, where $h_1,h_2,\cdots$ are…

Probability · Mathematics 2026-04-08 Francesco Concetti , Simone Franchini

Algorithms for generating random numbers that follow a gamma distribution with shape parameter less than unity are proposed. Acceptance-rejection algorithms are developed, based on the generalized exponential distribution. The squeeze…

Computation · Statistics 2024-11-18 Seiji Zenitani

The Poisson--Dirichlet distribution arises in many different areas. The parameter $\theta$ in the distribution is the scaled mutation rate of a population in the context of population genetics. The limiting case of $\theta$ approaching…

Probability · Mathematics 2008-11-12 Shui Feng , Fuqing Gao

Consider the following dynamic factor model: $\mathbf{R}_t=\sum_{i=0}^q \mathbf{\Lambda}_i \mathbf{f}_{t-i}+\mathbf{e}_t,t=1,...,T$, where $\mathbf{\Lambda}_i$ is an $n\times k$ loading matrix of full rank, $\{\mathbf{f}_t\}$ are i.i.d.…

Statistics Theory · Mathematics 2017-04-03 Z. D. Bai , Chen Wang , Ya Xue , Matthew Harding

The statistical distribution of the largest value drawn from a sample of a given size has only three possible shapes: it is either a Weibull, a Fr\'echet or a Gumbel extreme value distributions. I describe in this short review how to relate…

Statistical Mechanics · Physics 2020-09-22 Alex Hansen

Characterizations of finite sequences $\beta_{1}<\cdots<\beta_{n}$ representing expected values of order statistics from a random sample of size $n$ are given. As a by-product, a characterization of binomial mixtures, when the mixing random…

Probability · Mathematics 2022-05-02 A. Okolewski , N. Papadatos

In this work, we consider two sets of dependent variables $\{X_{1},\ldots,X_{n}\}$ and $\{Y_{1},\ldots,Y_{n}\}$, where $X_{i}\sim EW(\alpha_{i},\lambda_{i},k_{i})$ and $Y_{i}\sim EW(\beta_{i},\mu_{i},l_{i})$, for $i=1,\ldots, n$, which are…

Other Statistics · Statistics 2024-12-18 Ramkrishna Jyoti Samanta , Sangita Das , N. Balakrishnan
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