Asymptotic properties of the norm of extremum values of normal random elements in the space C[0, 1]

We prove that

On distribution of the norm for normal random elements in the space of continuous functions

We consider distributions of norms for normal random elements X in separable Banach spaces, in particular, in the space C(S) of continuous functions on a compact space S. We prove that, under some nondegeneracy condition, the functions $ {{\mathcal{F}}_X}=\left\{ {\mathrm{P}\left( {\left\| {X-z} \right\|\leqslant r} \right):\;z\in C(S)} \right\},\;r\geqslant 0 $ , are uniformly Lipschitz and that every separable Banach space B can be ε-renormed so that the family $ {{\mathcal{F}}_X} $ becomes uniformly Lipschitz in the new norm for any B-valued nondegenerate normal random element X.     

Asymptotic properties of minimum norm and optimal quadratures

Summary This paper contains three types of asymptotic results for certain quadratures applied to a Hilbert space of analytic functions. These results concern the following: bounds on the norm of a certain error functional; the convergence of the weights and nodes of a minimum norm quadrature to the weights and nodes of the corresponding Gaussian quadrature; and the convergence of optimal quadratures.The research reported in this document was supported by the Office of Naval Research, Contract Nonr 562(36) to Brown University and by the National Science Foundation, Grant GP 5906 to the University of Utah.On leave during the 1966–67 academic year at the Division of Applied Mathematics, Brown University, Providence, Rhode Island.     

Asymptotic values of strongly normal functions

Letf be meromorphic in the open unit discD and strongly normal; that is,

Asymptotic properties of random matrices and pseudomatrices

We study the asymptotics of sums of matricially free random variables, called random pseudomatrices, and we compare it with that of random matrices with block-identical variances. For objects of both types we find the limit joint distributions of blocks and give their Hilbert space realizations, using operators called ‘matricially free Gaussian operators’. In particular, if the variance matrices are symmetric, the asymptotics of symmetric blocks of random pseudomatrices agrees with that of symmetric random blocks. We also show that blocks of random pseudomatrices are ‘asymptotically matricially free’ whereas the corresponding symmetric random blocks are ‘asymptotically symmetrically matricially free’, where symmetric matricial freeness is obtained from matricial freeness by an operation of symmetrization. Finally, we show that row blocks of square, block-lower-triangular and block-diagonal pseudomatrices are asymptotically free, monotone independent and boolean independent, respectively.     

Asymptotic properties of digit sequences of random numbers

We consider several aspects of the relationship between a [0, 1]‐valued random variable X and the random sequence of digits given by its m‐ary expansion. We present results for three cases: (a) independent and identically distributed digit sequences; (b) random variables X with smooth densities; (c) stationary digit sequences. In the case of i.i.d. an integral limit thorem is proved which applies for example to relative frequencies, yielding asymptotic moment identities. We deal with occurrence probabilities of digit groups in the case that X has an analytic Lebesgue density. In the case of stationary digits we determine the distribution of X in terms of their transition functions. We study an associated [0, 1]‐valued Markov chain, in particular its ergodicity, and give conditions for the existence of stationary digit sequences with prespecified transition functions. It is shown that all probability measures induced on [0, 1] by such sequences are purely singular except for the uniform distribution. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Asymptotic behavior of empirical generating moment functions of random variables

Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 41, No. 7, pp. 992–994, July, 1989.     

Asymptotic properties of type I elliptical random vectors

Let be an elliptical random vector with a non-singular square matrix and a spherical random vector in , and let be a sequence of vectors in such that . We assume in this paper that the associated random radius R _k =(S ₁ + S ₂ +...+S _k )^1/2 is almost surely positive, and it has distribution function in the Gumbel max-domain of attraction. Relying on extreme value theory we obtain an exact asymptotic expansion of the tail probability for converging as to a boundary point. Further we discuss density convergence under a suitable transformation. We apply our results to obtain an asymptotic approximation of the distribution of partial excess above a high threshold, and to derive a conditional limiting result. Further, we investigate the asymptotic behaviour of concomitants of order statistics, and the tail asymptotics of associated random radius for subvectors of .      

Asymptotic normal distribution of multidimensional statistics of dependent random variables

A central limit theorem for multidimensional processes in the sense of [9], [10] is proved. In particular the asymptotic normal distribution of a sum of dependent random functions of m variables defined on the positive part of the integral lattice is established by the method of moments. The results obtained can be used, for example, in proving the asymptotic normality of different statistics of n₀-dependent random variables as well as to determine the asymptotic behaviour of the resultant of reflected waves of telluric type.     

Asymptotic properties of the occupation measure in a multidimensional skip-free Markov-modulated random walk

Queueing Systems - We consider a discrete-time d-dimensional process $$\{{\varvec{X}}_n\}=\{(X_{1,n},X_{2,n},\ldots ,X_{d,n})\}$$ on $${\mathbb {Z}}^d$$ with a background process $$\{J_n\}$$ on a...     

On the norm of random matrices

Translated from Matematicheskie Zametki, Vol. 57, No. 5, pp. 688–698, May, 1995.     

随机截断下部分线性模型中参数估计的渐近性质

考虑部分线性回归模型Yi＝xiβ g(ti) σiej,i＝1，2，…，n其中σi^2＝／f(ui)．当Yi因受某种随机干扰而被右截断时，就截断分布巳知的情形，利用所获得的截断观察数据构造了β，g，f的估计量β^～n，g^～n，f^～n，并在一定条件下，证明了β^～n的渐近正态性，同时得到了g^～n，f^～n的最优收敛速度。     

Asymptotic properties of supercritical branching processes in random environments

We consider a supercritical branching process （Zn） in an independent and identically distributed random environment ξ, and present some recent results on the asymptotic properties of the limit variable W of the natural martingale Wn = Zn/E[Zn｜ξ], the convergence rates of W - Wn （by considering the convergence in law with a suitable norming, the almost sure convergence, the convergence in Lp, and the convergence in probability）, and limit theorems （such as central limit theorems, moderate and large deviations principles） on （log Zn）.