The existence of smooth densities for the prediction filtering and smoothing problems

The qualitative behavior of buckled states of two different models of elastic beams is studied. It is assumed that random imperfections affect the governing nonlinear equations. It is shown that near the first critical value of the buckling load the stochastic bifurcation is described asymptotically by an algebraic equation whose coeffficients are Gaussian random variables. The corresponding asymptotic expansion for the displacement is to lowest order a Gaussian stochastic process.Work supported by NSF Grant No. DCR81-14726.Work supported by NSF Grant No. DMS87-01895.     

Communication games with partially soft power constraints

We consider a class of communication games which involves the transmission of a Gaussian random variable through a conditionally Gaussian memoryless channel in the presence of an intelligent jammer. The jammer is allowed to tap the channel and feed a correlated signal back into it. The transmitter-receiver pair is assumed to cooperate in minimizing some quadratic fidelity criterion while the jammer maximizes this same criterion. Security strategies which protect against irrational jammer behavior and which yield an upper bound on the cost are shown to exist for the transmitter-receiver pair over a class of fidelity criteria. Closed-form expressions for these strategies are provided in the paper, which are, in all cases but one, linear in the available information.This work was supported in part by the US Air Force under Grant No. AFOSR-84-0056 and in part by the Joint Services Electronics Program under Contract No. N00014-84-C-0149. An earlier version of this paper was presented at the 1986 IEEE Symposium on Information Theory, Ann Arbor, Michigan, 1986.     

A central limit theorem for fuzzy random variables

Fuzzy random variables have been proposed to treat situations in which both random behavior and fuzzy perception must be considered. A definition of independence is given for fuzzy random variables, as well as a notion of fuzzy Gaussian random variables. It is shown that a sum or mean of independent fuzzy random variables converges in the limit to a fuzzy Gaussian random variable, thus providing a fuzzy analogue of the central limit theorem of classical probability theory.     

Growth of Nanophase Clusters and Potential Energy Minima: Hysteresis, Oscillations, and Phase Transitions

The structures of small Lennard-Jones clusters (local and global minima) in the range n = 30 - 55 atoms are investigated during growth by random atom deposition using Monte Carlo simulations. The cohesive energy, average coordination number, and bond angles are calculated at different temperatures and deposition rates. Deposition conditions which favor thermodynamically stable (global minima) and metastable (local minima) are determined. We have found that the transition from polyicosahedral to quasicrystalline structures during cluster growth exhibits hysteresis at low temperatures. A minimum critical size is required for the evolution of the quasicrystalline family, which is larger than the one predicted by thermodynamics and depends on the temperature and the deposition rate. Oscillations between polyicosahedral and quasicrystalline structures occur at high temperatures in a certain size regime. Implications for the applicability of global optimization techniques to cluster structure determination are also discussed.     

Simple Examples of Lifschitz Tails in Gaussian Random Magnetic Fields

. This is a first attempt to investigate the asymptotic behavior of the integrated density of states at the infimum of the spectrum for Schrödinger operators with magnetic fields which are Gaussian random fields. In simple examples it is shown that the integrated density of states decays exponentially. These examples shall give a hint to consider in more general framework.     

Singularity among selfsimilar Gaussian random fields with different scaling parameters and others

Abstract

It is shown in this paper that the probability measures generated by selfsimilar Gaussian random fields are mutually singular, whenever they have different scaling parameters. So are those generated from a selfsimilar Gaussian random field and a stationary Gaussian random field. Certain conditions are also given for the singularity of the probability measures generated from two Gaussian random fields whose covariance functions are Schoenberg–Lévy kernels, and for those from stationary Gaussian random fields with spectral densities.     

A hyperbolic tangent identity and the geometry of Padé sign function iterations

The rational iterations obtained from certain Padé approximations associated with computing the matrix sign function are shown to be equivalent to iterations involving the hyperbolic tangent and its inverse. Using this equivalent formulation many results about these Padé iterations, such as global convergence, the semi-group property under composition, and explicit partial fraction decompositions can be obtained easily. In the second part of the paper it is shown that the behavior of points under the Padé iterations can be expressed, via the Cayley transform, as the combined result of a completely regular iteration and a chaotic iteration. These two iterations are decoupled, with the chaotic iteration taking the form of a multiplicative linear congruential random number generator where the multiplier is equal to the order of the Padé approximation.This research was supported in part by the National Science Foundation under Grant No. ECS-9120643, the Air Force Office of Scientific Research under Grant no. F49620-94-1-0104DEF, and the Office of Naval Research under Grant No. N00014-92-J-1706.     

Limit theorems for sums of dependent random variables occurring in statistical mechanics

Summary We study the asymptotic behavior of partial sums S _nfor certain triangular arrays of dependent, identically distributed random variables which arise naturally in statistical mechanics. A typical result is that under appropriate assumptions there exist a real number m, a positive real number , and a positive integer k so that (S _n–nm)/n^1–1/2k converges weakly to a random variable with density proportional to exp(–¦s¦ ^2k/(2k)!). We explain the relation of these results to topics in Gaussian quadrature, to the theory of moment spaces, and to critical phenomena in physical systems.Alfred P. Sloan Research Fellow. Research supported in part by a Broadened Faculty Research Grant at the University of Massachusetts and by National Science Foundation Grant MPS 76-06644Research supported in part by National Science Foundation Grants MPS 74-04870 A01 and MCS 77-20683     

Large deviations for diffusion processes in duals of nuclear spaces

Motivated by applications to neurophysiological problems, various authors have studied diffusion processes in duals of countably Hilbertian nuclear spaces governed by stochastic differential equations. In these models the diffusion coefficients describe the random stimuli received by spatially extended neurons. In this paper we present a large deviation principle for such processes when the diffusion terms tend to zero in terms of a small parameter. The lower bounds are established by making use of the Girsanov formula in abstract Wiener space. The upper bounds are obtained by Gaussian approximation of the diffusion processes and by taking advantage of the nuclear structure of the state space to pass from compact sets to closed sets.This research was partially supported by the National Science Foundation and the Air Force Office of Scientific Research Grant No. F49620-92-J-0154 and the Army Research Office Grant No. DAAL03-92-G-0008.     

Weak convergence of random sums and maximum random sums under nonrandom norming

Necessary and sufficient conditions are obtained for the weak convergence of random sums and maximum random sums of independent identically distributed random variables. Limit distributions for these sums are described. The indices are not assumed to be independent of the summands. Supported in part by Grants NFW000 and NFW300 from the International Science Foundation and the Russian Government and by Grant No. 93-011-1446 from the Russian Foundation for Fundamental Research. Proceedings of the XVII Seminar on Stability Problems for Stochastic Models, Kazan, Russia, 1995, Part I.     

Convergence rates for probabilities of moderate deviations for moving average processes

The present paper first shows that, without any dependent structure assumptions for a sequence of random variables, the refined results of the complete convergence for the sequence is equivalent to the corresponding complete moment convergence of the sequence. Then this paper investigates the convergence rates and refined convergence rates （or complete moment convergence） for probabilities of moderate deviations of moving average processes. The results in this paper extend and generalize some well-known results.     

Testing for multiple change points

In this paper we concentrate on testing for multiple changes in the mean of a series of independent random variables. Suggested method applies a maximum type test statistic. Our primary focus is on an effective calculation of critical values for very large sample sizes comprising (tens of) thousands of observations and a moderate to large number of segments. To that end, Monte Carlo simulations and a modified Bellman’s principle of optimality are used. It is shown that, indisputably, computer memory becomes a critical bottleneck in solving a problem of such a size. Thus, minimization of the memory requirements and appropriate order of calculations appear to be the keys to success. In addition, the formula that can be used to get approximate asymptotic critical values using the theory of exceedance probability of Gaussian fields over a high level is presented.     

On decoupling,series expansions,and tail behavior of chaos processes

Some important aspects of chaos random variables such as decoupling, an almost sure representation (a Karhunen-Loeve expansion) and integrability are discussed here, the first being a tool for, and the third as a consequence of, the second. The main goal in this note is to learn about the structure of the limit laws ofU-processes.Research partially supported by National Science Foundation Grant No. DMS-9000132 and University of Connecticut Grant No. G12-913501.     

相伴的高斯随机变量序列的一个强不变原理

本文利用鞅的Skorohod表示, 在序列是高斯的且序列的协方差系数以幂指数速度递减的条件下,证明了相伴高斯随机变量序列的一个强不变原理\bd 作为推论得到了相伴高斯随机变量序列的重对数律和钟重对数律     

Representations of Gaussian Random Fields and Approximation of Elliptic PDEs with Lognormal Coefficients

Approximation of elliptic PDEs with random diffusion coefficients typically requires a representation of the diffusion field in terms of a sequence \(y=(y_j)_{j\ge 1}\) of scalar random variables. One may then apply high-dimensional approximation methods to the solution map \(y\mapsto u(y)\). Although Karhunen–Loève representations are commonly used, it was recently shown, in the relevant case of lognormal diffusion fields, that multilevel-type expansions may yield better approximation rates. Motivated by these results, we construct wavelet-type representations of stationary Gaussian random fields defined on arbitrary bounded domains. The size and localization properties of these wavelets are studied, and used to obtain polynomial approximation results for the related elliptic PDE which outperform those achievable when using Karhunen–Loève representations. Our construction is based on a periodic extension of the stationary random field, and the expansion on the domain is then obtained by simple restriction. This makes the approach easily applicable even when the computational domain of the PDE has a complicated geometry. In particular, we apply this construction to the class of Gaussian processes defined by the family of Matérn covariances. The proposed periodic continuation technique has other relevant applications such as fast simulation of trajectories. It can be regarded as a continuous analog of circulant embedding techniques introduced for Toeplitz matrices. One of its specific features is that the rate of decay of the eigenvalues of the covariance operator of the periodized process provably matches that of the Fourier transform of the covariance function of the original process.     

The asymptotic distribution of the maxima of a Gaussian random field on a lattice

Under mild conditions on the covariance function of a stationary Gaussian process, the maxima behaves asymptotically the same as the maxima of independent, identically distributed Gaussian random variables. In order to achieve extremal clustering, Hsing et al. (Ann Appl Probab 6:671–686, 1996) considered a triangular array of Gaussian sequences in which the correlation between “neighboring” observations approaches 1 at a certain rate. Using analogues of the conditions of Hsing et al., which allows for strong local dependence among variables but asymptotic independence, it is possible to show that two-dimensional Gaussian random fields also exhibit extremal clustering in the limit. A closed form expression for the extremal index governing the clustering will be provided. The results apply to Gaussian random fields in which the spatial domain is rescaled.     

Limit theorems for sums of dependent random variables occurring in statistical mechanics

Summary By the use of conditioning, we extend previously obtained results on the asymptotic behavior of partial sums for certain triangular arrays of dependent random variables, known as Curie-Weiss models. These models arise naturally in statistical mechanics. The relation of these results to multiple phases, metastable states, and other physical phenomena is explained.Alfred P. Sloan Research Fellow. Research supported in part by National Science Foundation Grant MPS 76-06644A01Alfred P. Sloan Research Fellow. Research supported in part by National Science Foundation Grant MCS 77-20683 and by U.S.-Israel Binational Science FoundationResearch supported in part by National Science Foundation Grant PHY77-02172     

Small ball probabilities for Gaussian random fields and tensor products of compact operators

We find the logarithmic -small ball asymptotics for a large class of zero mean Gaussian fields with covariances having the structure of ``tensor product'. The main condition imposed on marginal covariances is the regular behavior of their eigenvalues at infinity that is valid for a multitude of Gaussian random functions including the fractional Brownian sheet, Ornstein - Uhlenbeck sheet, etc. So we get the far-reaching generalizations of well-known results by Csáki (1982) and by Li (1992). Another class of Gaussian fields considered is the class of additive fields studied under the supremum-norm by Chen and Li (2003). Our theorems are based on new results on spectral asymptotics for the tensor products of compact self-adjoint operators in Hilbert space which are of independent interest.

     

The almost sure behavior of the oscillation modulus for PL-process and cumulative hazard process under random censorship

The local behavior of oscillation modulus of the product-limit (PL) process and the cumulative hazard process is investigated when the data are subjected to random censoring. Laws of the iterated logarithm of local oscillation modulus for the PL-process and the cumulative hazard process are established. Some of these results are applied to obtain the almost sure best rates of convergence for various types of density estimators as well as the Bahadur-Kiefer type process. Project supported in part by the National Natural Science Foundation of China (Grant No. 19701037).     

Regularity,continuity and approximation of isotropic Gaussian random fields on compact two-point homogeneous spaces

Gaussian random fields defined over compact two-point homogeneous spaces are considered and Sobolev regularity and Hölder continuity are explored through spectral representations. It is shown how spectral properties of the covariance function associated to a given Gaussian random field are crucial to determine such regularities and geometric properties. Furthermore, fast approximations of random fields on compact two-point homogeneous spaces are derived by truncation of the series expansion, and a suitable bound for the error involved in such an approximation is provided.