Functional Quantization and Small Ball Probabilities for Gaussian Processes

Quantization consists in studying the L ^r -error induced by the approximation of a random vector X by a vector (quantized version) taking a finite number n of values. We investigate this problem for Gaussian random vectors in an infinite dimensional Banach space and in particular, for Gaussian processes. A precise link proved by Fehringer⁽⁴⁾ and Dereich et al. ⁽³⁾ relates lower and upper bounds for small ball probabilities with upper and lower bounds for the quantization error, respectively. We establish a complete relationship by showing that the same holds for the direction from the quantization error to small ball probabilities. This allows us to compute the exact rate of convergence to zero of the minimal L ^r -quantization error from logarithmic small ball asymptotics and vice versa.     

πNN过程对Gottfried求和规则破坏的贡献

指出在计算πNN过程对核子结构函数的贡献时,将反冲核子视为物理核子更为合理.对Gottfried求和规则破坏、对质子中海夸克分布u和d不对称u(x)–d(x)等方面的贡献给出了详细的计算,与已有实验进行比较.结果表明π介子交换不是导致这一破坏以及影响核子结构函数的效应的惟一来源.     

Gauge Dependence of the Effective Gravitational Field

We analyze the gauge ambiguity problem for the effective gravitational field from the standpoint of the measurement process. The motion of a test point particle playing the role of a measuring device is investigated in the field of a point gravitating mass in the one-loop approximation. We show that the gravitational field value determined from the effective equations of motion of the device explicitly depends on the Feynman gauge parameter. This dependence is essential in the sense that a gauge variation cannot be interpreted as a deformation of the reference frame, which leads to a gauge ambiguity in the values of observed quantities. In particular, this result disproves the hypothesis that gauge dependence is canceled in the effective equations of motion of a classical point particle.     

流形上随机流的渐近平坦性

本文的目的是讨论流形上由随机微分方程确定的扩散过程的体积零化性质。令X_t(x)是描述流形M上的微分同胚流x→X_t(x)的扩散过程,K是M中具有正有限Hausdorff测度的紧致曲面,我们给出X_t(K)的面积在t→∞时几乎必然趋于零的条件,特别地,随机流X_t(·)的渐近零化定向可求长弧r:[0,1]→M的弧长。     

Information and asymptotic efficiency of the case-cohort sampling design in Cox's regression model

Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the case-cohort sampling design in the proportional hazards regression model are studied. The asymptotic information and lower bound for estimating the parametric regression parameter are calculated based on the effective score, which is obtained by determining the component of the parametric score orthogonal to the space generated by the infinite-dimensional nuisance parameter. The asymptotic distributions of the maximum pseudolikelihood and related estimators in an i.i.d. setting show that these estimators do not achieve the computed asymptotic lower bound. Simple guidelines are provided to determine in which instances such estimators are close enough to efficient for practical purposes.     

HILBERTIAN INVARIANCE PRINCIPLE FOR EMPIRICAL PROCESS ASSOCIATED WITH A MARKOV PROCESS

The authors establish the Hilbertian invariance principle for the empirical process of a stationary Markov process, by extending the forward-backward martingale decomposition of Lyons-Meyer-Zheng to the Hilbert space valued additive functionals associated with general non-reversible Markov processes.     

Regression quantiles for unstable autoregressive models

This paper investigates regression quantiles (RQ) for unstable autoregressive models. The uniform Bahadur representation of the RQ process is obtained. The joint asymptotic distribution of the RQ process is derived in a unified manner for all types of characteristic roots on or outside the unit circle. It involves stochastic integrals in terms of a sequence of independent and identically distributed multivariate Brownian motions with correlated components. The related L-estimator is also discussed. The asymptotic distributions of the RQ and the L-estimator corresponding to the nonstationary componentwise arguments can be transformed into a function of a normal random variable and a sequence of i.i.d. univariate Brownian motions. This is different from the analysis based on the LSE in the literature. As an auxiliary theorem, a weak convergence of a randomly weighted residual empirical process to the stochastic integral of a Kiefer process is established. The results obtained in this paper provide an asymptotic theory for nonstationary time series processes, which can be used to construct robust unit root tests.     

Diversification of shotgun process

Three protocols for shotgun process are put forth in which simultaneous multi-fold reactions occur exclusively to each other. The first one involves simple combination of selective and non-selective reactions. Even if the simple protocol fails to give rise to the high selectivity, satisfactory outcome can be achieved by kinetic control or adjustment of functional groups.     

A strong law of large numbers for generalized random sets from the viewpoint of empirical processes

In this article we prove a strong law of large numbers for Borel measurable nonseparably valued random elements in the case of generalized random sets.

     

Time-Reversed Dynamical Entropy and Irreversibility in Markovian Random Processes

A concept of time-reversed entropy per unit time is introduced in analogy with the entropy per unit time by Shannon, Kolmogorov, and Sinai. This time-reversed entropy per unit time characterizes the dynamical randomness of a stochastic process backward in time, while the standard entropy per unit time characterizes the dynamical randomness forward in time. The difference between the time-reversed and standard entropies per unit time is shown to give the entropy production of Markovian processes in nonequilibrium steady states.