Small Ball Probabilities Around Random Centers of Gaussian Measures and Applications to Quantization

Let be a centered Gaussian measure on a separable Hilbert space (E, ). We are concerned with the logarithmic small ball probabilities around a -distributed center X. It turns out that the asymptotic behavior of –log (B(X,)) is a.s. equivalent to that of a deterministic function _R (). These new insights will be used to derive the precise asymptotics of a random quantization problem which was introduced in a former article by Dereich, Fehringer, Matoussi, and Scheutzow.⁽⁸⁾     

Asymptotically Efficient Nonparametric Estimation of Nonlinear Spectral Functionals

The paper considers a problem of construction of asymptotically efficient estimators for functionals defined on a class of spectral densities. We define the concepts of H ₀- and IK-efficiency of estimators, based on the variants of Hájek–Ibragimov–Khas'minskii convolution theorem and Hájek–Le Cam local asymptotic minimax theorem, respectively. We prove that is a suitable sequence of T ^1/2-consistent estimators of unknown spectral density (), is H ₀- and IK-asymptotically efficient estimator for a nonlinear smooth functional ().     

Perturbation of Wigner matrices and a conjecture

Let be an arbitrary self-adjoint matrix and be an (random) Wigner matrix. We show that is positive definite in the average. This partially answers a long-standing conjecture. On the basis of asymptotic freeness our result implies that is positive definite whenever the noncommutative random variables and are in free relation, with semicircular.

     

The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother

A Gaussian-sum smoother is developed based on the two filter formula for smoothing. This facilitates the application of non-Gaussian state space modeling to diverse problems in time series analysis. It is especially useful when a higher order state vector is required and the application of the non-Gaussian smoother based on direct numerical computation is impractical. In particular, applications to the non-Gaussian seasonal adjustment of economic time series and to the modeling of seasonal time series with several outliers are shown.     

双频回旋管内置准光模式变换器设计

回旋管一般使用准光模式变换器实现高阶腔体模式到高斯波束的转换。结合标量衍射理论、KS迭代算法、几何光学、最小均方法等方法设计了工作频率为140 GHz（TE_{24, 9}）和105 GHz（TE_{18, 7}）的双频准光模式变换器。仿真结果显示所设计的准光模式变换器工作频率为140 GHz（TE_{24, 9}）时能量传输效率99.0%、高斯含量99.7%,工作频率为105 GHz（TE_{18, 7}）时能量传输效率97.3%、高斯含量98.0%。能够满足MW级双频回旋管的应用需求。

     

Some pansystems studies on panchaos and strange panattractor

Paper [1] discussed the relations among panchaos and strange panattractor and pansystems operators. Paper [2] gave the applications of the fixed-point pansystems theorems to these typical nonlinear problems. In this paper, we firstly present several concepts: increasing relation, maximal panchaos, etc., and discuss the relations among them. We also discuss the problems when two increasing relations have the same panchaos and when panchaos of g is panchaos of g^t as well.     

高斯曲率为已给六次函数的旋转曲面

讨论了R^3内高斯曲率K（s）为已给六次函数的旋转曲面的存在性问题,并给出了在定义域范围内这类曲面的位置向量场,该结果将陈霞研究结果中K（s）从四次函数提高到了六次函数．     

On a Test Statistic for Linear Trend

Let {W(s)} _s 0 be a standard Wiener process. The supremum of the squared Euclidian norm Y (t)², of the R²-valued process Y(t)=(1/t W(t), {12/t ³ int_{0 t} s dW (s)– {3/t} W(t)), t [, 1], is the asymptotic, large sample distribution, of a test statistic for a change point detection problem, of appearance of linear trend. We determine the asymptotic behavior P {sup _t [, 1] Y(t)² > u as u , of this statistic, for a fixed (0,1), and for a moving = (u) 0 at a suitable rate as u . The statistical interest of our results lie in their use as approximate test levels.     

On Large Deviations of Vector-Valued Additive Functions of a Markov Gaussian Field

We prove a local limit theorem for large deviations of the sums , where , is a Markov Gaussian random field, is a bounded vector-valued function, and . This paper generalizes the paper [13].     

Tail behaviour of Gaussian processes with applications to the Brownian pillow

In this paper we investigate the tail behaviour of a random variable S which may be viewed as a functional T of a zero mean Gaussian process X, taking special interest in the situation where X obeys the structure which is typical for limiting processes occurring in nonparametric testing of (multivariate) independency and (multivariate) constancy over time. The tail behaviour of S is described by means of a constant a and a random variable R which is defined on the same probability space as S. The constant a acts as an upper bound, and is relevant for the computation of the efficiency of test statistics converging in distribution to S. The random variable R acts as a lower bound, and is instrumental in deriving approximation for the upper percentage points of S by simulation.