Estimation and Simulation of Autoregressive Hilbertian Processes with Exogenous Variables

We present the autoregressive Hilbertian with exogenous variables model (ARHX) which intends to take into account the dependence structure of random curves viewed as H-valued random variables, where H is a Hilbert space of functions, under the influence of explanatory variables. Limit theorems and consistent estimators are derived from an autoregressive representation. A simulation study illustrates the accuracy of the estimation by making a comparison on forecasts with other functional models.     

Inference in hybrid Bayesian networks using mixtures of polynomials

The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixture of polynomials (MOP) approximations of probability density functions (PDFs). Hybrid BNs contain a mix of discrete, continuous, and conditionally deterministic random variables. The conditionals for continuous variables are typically described by conditional PDFs. A major hurdle in making inference in hybrid BNs is marginalization of continuous variables, which involves integrating combinations of conditional PDFs. In this paper, we suggest the use of MOP approximations of PDFs, which are similar in spirit to using mixtures of truncated exponentials (MTEs) approximations. MOP functions can be easily integrated, and are closed under combination and marginalization. This enables us to propagate MOP potentials in the extended Shenoy-Shafer architecture for inference in hybrid BNs that can include deterministic variables. MOP approximations have several advantages over MTE approximations of PDFs. They are easier to find, even for multi-dimensional conditional PDFs, and are applicable for a larger class of deterministic functions in hybrid BNs.     

Infinite Divisibility of Random Objects in Locally Compact Positive Convex Cones

Random objects taking on values in a locally compact second countable convex cone are studied. The convex cone is assumed to have the property that the class of continuous additive positively homogeneous functionals is separating, an assumption which turns out to imply that the cone is positive. Infinite divisibility is characterized in terms of an analog to the Lévy–Khinchin representation for a generalized Laplace transform. The result generalizes the classical Lévy–Khinchin representation for non-negative random variables and the corresponding result for random compact convex sets inRⁿ. It also gives a characterization of infinite divisibility for random upper semicontinuous functions, in particular for random distribution functions with compact support and, finally, a similar characterization for random processes on a compact Polish space.     

Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports

Characterising intractable high-dimensional random variables is one of the fundamental challenges in stochastic computation. The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables. This paper generalises the functional tensor-train approximation of the inverse Rosenblatt transport recently developed by Dolgov et al. (Stat Comput 30:603–625, 2020) to a wide class of high-dimensional non-negative functions, such as unnormalised probability density functions. First, we extend the inverse Rosenblatt transform to enable the transport to general reference measures other than the uniform measure. We develop an efficient procedure to compute this transport from a squared tensor-train decomposition which preserves the monotonicity. More crucially, we integrate the proposed order-preserving functional tensor-train transport into a nested variable transformation framework inspired by the layered structure of deep neural networks. The resulting deep inverse Rosenblatt transport significantly expands the capability of tensor approximations and transport maps to random variables with complicated nonlinear interactions and concentrated density functions. We demonstrate the efficiency of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.

     

Sur certaines martingales de Mandelbrot

We consider a non-negative martingale, defined by sums of product of non-negative random weights indexed by nodes of a Galton-Watson tree. In case the limit variable is not degenerate, we study the asymptotic behaviour at infinity of its distribution; in the contrary case, we prove that there is an associated natural martingale which converges to a non-negative random variable with infinite mean. The two limit variables satisfy the same distributional equation.     

Permutation-invariance in Komlós type theorem for non-negative random variables

We provide a permutation-invariant version of Komlós’ type convergence for non-negative random variables.     

A class of models for uncorrelated random variables

We consider the class of multivariate distributions that gives the distribution of the sum of uncorrelated random variables by the product of their marginal distributions. This class is defined by a representation of the assumption of sub-independence, formulated previously in terms of the characteristic function and convolution, as a weaker assumption than independence for derivation of the distribution of the sum of random variables. The new representation is in terms of stochastic equivalence and the class of distributions is referred to as the summable uncorrelated marginals (SUM) distributions. The SUM distributions can be used as models for the joint distribution of uncorrelated random variables, irrespective of the strength of dependence between them. We provide a method for the construction of bivariate SUM distributions through linking any pair of identical symmetric probability density functions. We also give a formula for measuring the strength of dependence of the SUM models. A final result shows that under the condition of positive or negative orthant dependence, the SUM property implies independence.     

On linear programs with random costs

We consider linear programs in which the objective function (cost) coefficients are independent non-negative random variables, and give upper bounds for the random minimum cost. One application shows that for quadratic assignment problems with such costs certain branch-and-bound algorithms usually take more than exponential time.     

Inventory management under highly uncertain demand

We show that base-stock levels first increase and then decrease as the standard deviation increases for a variety of non-negative random variables with a given mean and provide a distribution-free upper bound for optimal base-stock levels that grows linearly with the standard deviation and then remains constant.     

独立重尾随机变量随机和的大偏差估计

本文研究了一类独立重尾随机变量随机和S（t)∧=∑k=1^N(t)Xk,t≥0的大偏差概率，其中{N(t),t≥0}是一放大晨负整数值随机变量；{Xn,n≥1}是非负，独立随机变量序列，并与{N(t),t≥０}独立。本文的结果将{Xn,n≥１}为独立同分布情形推广到了独立不同分布情形。     

重尾平稳序列的大偏差

本文给出了一类重尾的随机变量序列{Xn,n≥1}的部分和Sn=∑i=1 n Xi与随机和S(t)=∑i=1^N(t) Xi的大偏差结果其中{N(t),t≥)}是一族非负整值的随机变量，{Xn,n≥1}是非负的平稳过程，并且与{N(t),t≥0}独立。本文将独立同分布情形的结果掖到了平稳相依的情形。     

Measures of the functional dependence of random vectors

In this work, we define a set of properties that any measure of functional dependence that exists between random vectors should possess. We also construct measures of functional dependence and show that they satisfy the properties mentioned above. Relationships between these measures and previously defined measures of functional dependence between random variables are discussed.     

A probabilistic comparison of the strength of split, triangle, and quadrilateral cuts

We consider mixed integer linear sets defined by two equations involving two integer variables and any number of non-negative continuous variables. We analyze the benefit from adding a non-split inequality on top of the split closure. Applying a probabilistic model, we show that the importance of a type 2 triangle inequality decreases with decreasing lattice width, on average. Our results suggest that this is also true for type 3 triangle and quadrilateral inequalities.     

Limit theorems for records from discrete distributions

Weak and strong functional limit theorems are obtained for record values and record epochs in a sequence of independent random variables with common distribution F. The emphasis is on the case in which F is concentrated on the non-negative integers. For contrast, the well-known case of continuous F is also considered. Analogues of results obtained earlier by Resnick, de Haan and the author for continuous F are presented here for F concentrated on the non-negative integers. Also is investigated under which circumstances the latter case is so close to the continuous F case that the resulting limit theorems are the same.     

A Characterization of Joint Distribution of Two-Valued Random Variables and Its Applications

We obtain an explicit representation for joint distribution of two-valued random variables with given marginals and for a copula corresponding to such random variables. The results are applied to prove a characterization of r-independent two-valued random variables in terms of their mixed first moments. The characterization is used to obtain an exact estimate for the number of almost independent random variables that can be defined on a discrete probability space and necessary conditions for a sequence of r-independent random variables to be stationary.     

On the Bahadur representation of sample quantiles for sequences of φ-mixing random variables

The object of the present investigation is to show that the elegant asymptotic almost-sure representation of a sample quantile for independent and identically distributed random variables, established by Bahadur [1] holds for a stationary sequence of φ-mixing random variables. Two different orders of the remainder term, under different φ-mixing conditions, are obtained and used for proving two functional central limit theorems for sample quantiles. It is also shown that the law of iterated logarithm holds for quantiles in stationary φ-mixing processes.     

关于大偏差概率的一个界

研究得到了关于随机和S(t)=∑N(t)i=1Xi,t≥0大偏差的幂的一个界,其中(N(t))t≥0是一族非负整值随机变量,(Xn)n∈N是独立同分布的随机变量,其共同的分布函数是F与(N(t))t≥0独立.本结论是在假设分布函数F的右尾属于ERV族的情况下得到的.     

Weak Characterizations of Stochastic Integrability and Dudley’s Theorem in Infinite Dimensions

In this paper we consider stochastic integration with respect to cylindrical Brownian motion in infinite-dimensional spaces. We study weak characterizations of stochastic integrability and present a natural continuation of results of van Neerven, Weis and the second named author. The limitation of weak characterizations will be demonstrated with a nontrivial counterexample. The second subject treated in the paper addresses representation theory for random variables in terms of stochastic integrals. In particular, we provide an infinite-dimensional version of Dudley’s representation theorem for random variables and an extension of Doob’s representation for martingales.     

一个复合风险模型的引入及其大偏差估计的建立

本文研究了关于独立随机和精大偏差的估计问题,改进了文献[4,7]的结果。首先我们引入了一个比过去工作更现实复合更新风险模型,然后在该模型下建立了与文献中完全相同的精大偏差结果。