算子稳定随机向量序列的一些极限结果

对于独立同分布的没有Gauss分量的指数为可逆线性算子A的算子稳定的R~d值随机向量序列,本文通过积分检验讨论了其部分和及加权和(包括一些经典的加权和,如Cesàro加权和,后置和方式,Euler可和方式,Borel可和方式,几何加权和等)的极限结果.由此得到了部分和及加权和在相对于A的谱分解下的Chover型重对数律,这是与A的特征值的实部有关的结果.     

Geometric stable laws: Estimation and applications

Geometric stable laws constitute a class of limiting distributions of appropriately normalized random sums of i.i.d. random variables. We consider the problem of estimation of the parameters of univariate and multivariate geometric stable laws. Our estimation technique is based on the method of moments and yields consistent and asymptotically normal estimators. We apply our estimators to a currency exchange data and show that the geometric stable dominates Paretian stable and normal models.     

Limit theorems for functionals of Gaussian vectors

Operator self-similar processes, as an extension of self-similar processes, have been studied extensively. In this work, we study limit theorems for functionals of Gaussian vectors. Under some conditions, we determine that the limit of partial sums of functionals of a stationary Gaussian sequence of random vectors is an operator self-similar process.     

Weak Limits for Multivariate Random Sums

Let {Xi, i1} be a sequence of i.i.d. random vectors inRd, and letνp, 0<p<1, be a positive, integer valued random variable, independent ofXis. Theν-stable distributions are the weak limits of properly normalized random sums ∑νpi=1 Xiasνp ∞ andpνp ν. We study the properties ofν-stable laws through their representation via stable laws. In particular, we estimate their tail probabilities and provide conditions for finiteness of their moments.     

Convergence in law of random sums with non-random centering

We extend results obtained in Kruglov,⁽⁷⁾ and Finkelstein and Tucker⁽³⁾ to obtain necessary and sufficient conditions for convergence in law of random sums of non-identically distributed independent random variables under non-random centering. Thei.i.d. case is also considered for random variables attracted to a stable law. Necessary and sufficient conditions for convergence in law of these random variables under non-random centering, and in some cases, under non-random norming, are also obtained. The distribution functions for the limit laws are determined as well, generalizing results of Robbins.⁽¹⁰⁾ Supported in part by The State University of New York and United States Information Agency Grant No. IA AEMP69193692.     

Moments of Limits of Lightly Trimmed Sums of Random Vectors in the Generalized Domain of Normal Attraction of Non-Gaussian Operator-Stable Laws

Existence and nonexistence for moments of limiting random vectors of normalized, lightly trimmed sums of random vectors in the generalized domain of normal attraction of non-Gaussian operator-stable laws are studied. The idea of representing the limiting random vectors by infinite series is essentially used in the proofs.     

Geometric stable distributions in Banach spaces

Geometric stable laws have become an object of attention in recent publications dealing with heavy tailed modeling. Many applications require understanding geometric stable laws on infinite dimensional spaces. This paper studies geometric stable laws on Banach spaces, and their place in the more general family of geometric infinitely divisible laws. Furthermore, we discuss rates of convergence in the domains of attraction of geometric stable laws in Banach spaces.Research was supported by NSF Grant DMS-9103452 and a NATO Scientific Affairs Division Grant CRG 900798. University of California, at Santa Barbara, Santa Barbara, California 93102.Research was supported by ONR Grant N00014-90-J-1287 and United States Israel Binational Science Foundation. School of Operations Research, and Industrial Engineering, Cornell University, Ithaca, New York 14853.     

Sample Covariance Matrix for Random Vectors with Heavy Tails

We compute the asymptotic distribution of the sample covariance matrix for independent and identically distributed random vectors with regularly varying tails. If the tails of the random vectors are sufficiently heavy so that the fourth moments do not exist, then the sample covariance matrix is asymptotically operator stable as a random element of the vector space of symmetric matrices.     

Extreme Values and the Multivariate Compact Law of the Iterated Logarithm

For a sequence of independent identically distributed Euclidean random vectors, we prove a compact Law of the iterated logarithm when finitely many maximal terms are omitted from the partial sum. With probability one, the limiting cluster set of the appropriately operator normed partial sums is the closed unit Euclidean ball. The result is proved under the hypotheses that the random vectors belong to the Generalized Domain of Attraction of the multivariate Gaussian law and satisfy a mild integrability condition. The integrability condition characterizes how many maximal terms must be omitted from the partial sum sequence.     

对数似然比与整值随机变量序列的一类强律

本文引进对数似然比作为整值随机变量序列相对于服从几何分布的独立随机变量序列的偏差的一种度量,并通过限制对数似然比给出了样本空间的一个子集.在此子集上得到了一类用不等式表示的强律,其中包含整值随机变量序列与相对熵密度及几何分布的熵函数有关的若干极限性质.     

Sample Path Properties of Generalized Random Sheets with Operator Scaling

Journal of Theoretical Probability - We consider operator scaling $$\alpha $$ -stable random sheets, which were introduced in Hoffmann (Operator scaling stable random sheets with application to...     

Limiting behavior of weighted sums with stable distributions

We present an integral test to determine the limiting behavior of weighted sums of independent, symmetric random variables with stable distributions, and deduce Chover-type laws of the iterated logarithm for them.     

Exponential Mixture Representation of Geometric Stable Distributions

We show that every strictly geometric stable (GS) random variable can be represented as a product of an exponentially distributed random variable and an independent random variable with an explicit density and distribution function. An immediate application of the representation is a straightforward simulation method of GS random variables. Our result generalizes previous representations for the special cases of Mittag-Leffler and symmetric Linnik distributions.     

A Compact Law of the Iterated Logarithm for Random Vectors in the Generalized Domain of Attraction of the Multivariate Gaussian Law

For a sequence of independent identically distributed random vectors, we prove that the limiting cluster set of the appropriately operator normed partial sums is, with probability one, the closed unit euclidean ball. The result is proved under the hypotheses that the law of the random vectors belongs to the Generalized Domain of Attraction of the multivariate Gaussian law and satisfy a mild integrability condition. The two conditions together are still weaker than finite second normed moment and are necessary and sufficient.     

The Probability Content of Cones in Isotropic Random Fields

This paper provides computable representations for the evaluation of the probability content of cones in isotropic random fields. A decomposition of quadratic forms in spherically symmetric random vectors is obtained and a representation of their moments is derived in terms of finite sums. These results are combined to obtain the distribution function of quadratic forms in spherically symmetric or central elliptically contoured random vectors. Some numerical examples involving the sample serial covariance are provided. Ratios of quadratic forms are also discussed.     

Certain Probabilistic Aspects of Semistable Laws

We study the extent to which the property of semistability of a random vector in ^d is determined by semistability of its marginals, and the place of semistable laws within the family of type G and sub-stable laws. Similarities and differences between stable and semistable laws are discussed.     

On the laws of large numbers for nonnegative random variables

Strong laws of large numbers concerning nonnegative random variables are obtained and then they are utilized to establish stability results, among other things, for sums of pairwise independent random variables and the range of random walks.     

Complex Stable Sums of Complex Stable Random Variables

A definition of complex stable random variables is presented which includes earlier definitions as special cases. The class of complex stable random variables is characterized and is shown to be a subclass of the operator stable random variables. The exact conditions under which a sum of independent complex stable random variables is again complex stable are also found.     

Stable and Semistable Hemigroups: Domains of Attraction and Self-Decomposability

A hemigroup of probability measures builds the set of distributions of the increments of an independent increment process. Decomposability properties of the hemigroup lead to stable respectively semistable hemigroups and enable us to show that such hemigroups appear as limits of certain functional limit theorems for operator-normed independent (non-identically) distributed random vectors. Regular variation properties of the norming operators show that the functional limit theorems are closely related to limits of infinitesimal triangular arrays of independent random vectors, i.e., to operator-self-decomposable respectively operator-semi-self-decomposable laws.     

Strong Law of Large Numbers with Operator Normalizations for Martingales and Sums of Orthogonal Random Vectors

We establish the strong law of large numbers with operator normalizations for vector martingales and sums of orthogonal random vectors. We describe its applications to the investigation of the strong consistency of least-squares estimators in a linear regression and the asymptotic behavior of multidimensional autoregression processes.