分析Chiral分层媒质特性的一般方法(英文)

提出了一套系统方法用于求解任意导体片位于界面的Chiral分层媒质的谐振频率,衰减因子和电磁场分布。文中的分析是在Fourier变换域的并矢格林函数和矩阵分析的基础上来进行的,给出的公式的最大价值在于它提供了求解Chiral分层媒质问题的方法论。     

一类边值问题临界解的扰动性质

在LP(1 p <+∞ ) 空间中证明了一类非齐次积一秩微分方程边值问题的临界解关于方程系数和区域尺寸的连续依赖性 ,并获得了相应的高阶扰动公式     

Nyquist采样频率下的频谱失真与采样定理

本文通过在Nyquist采样率下对信号的频谱是否发生失真，发生了怎样的失真的讨论，分析了在进行离散傅立叶变换（DFT）时，采样定理在其边界条件下进行信号处理时存在的问题。展示了在该条件下频谱发生严重失真的形式和程度，最后从无失真地恢复时域信号x(t）的角度讨论了DFT意义下采样定理的表述。     

块Jacobi迭代阵的收敛性

研究大型线性方程组迭代解法中分块ＪＡＣＯＢＩ迭代阵的收敛性。采用块矩阵分析方法和谱半径降维估计法得到块Ｊａｃｏｂｉ迭代阵收敛的实用充分条件。     

Spectral Estimation of Covolatility from Noisy Observations Using Local Weights

We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes, which are observed discretely with additive observation noise. The appropriate estimation for time‐varying volatilities is based on an asymptotic equivalence of the underlying statistical model to a white‐noise model with correlation and volatility processes being constant over small time intervals. The asymptotic equivalence of the continuous‐time and discrete‐time experiments is proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier non‐parametric methods in the literature for the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between various proposed methods.     

一类弹性系统的内部精确能控性

该文考虑的是具有分布控制的Timoshenko梁系统的精确能控性.用谱分解方法建立了保守系统的正反向能量不等式,证明了在通常的能量空间中该系统是精确能控的.     

Geometric Anisotropic Spatial Point Pattern Analysis and Cox Processes

We consider spatial point processes with a pair correlation function, which depends only on the lag vector between a pair of points. Our interest is in statistical models with a special kind of ‘structured’ anisotropy: the pair correlation function is geometric anisotropic if it is elliptical but not spherical. In particular, we study Cox process models with an elliptical pair correlation function, including shot noise Cox processes and log Gaussian Cox processes, and we develop estimation procedures using summary statistics and Bayesian methods. Our methodology is illustrated on real and synthetic datasets of spatial point patterns.     

Bayesian autoregressive spectral analysis

The article describes an operational Bayesian approach to making inferences for the spectral density function for univariate autoregressive processes and for the AR operator of multivariate autoregressive processes. The derivation of the approach is described. Numerical examples, including the Wolfer Sunspot numbers, are used to demonstrate the practical usefulness of the approach.     

Automatic selection of a linear predictor through frequency domain cross-validation

Given data from a weakly stationary stochastic process in discrete time, and any L-step ahead linear predictor estimated from that data, we will construct an approximately unbiased estimator of the resulting mean squared error of L-step ahead linear prediction. The motivation for the estimator is based on frequency domain cross-validation, and hence the range of validity and applicability of the resulting selection method is not limited by particular assumptions about the structure of the underlying stochastic process or the form of the fitted linear predictors. We also propose a new frequency domain predictor fitting method. The method provides a natural finite-past analog to the existing spectral factorization techniques, and it compares favorably with the existing techniques, both asymptotically and for finite samples. In a Monte Carlo study, we compare several predictor selection methods, at lead times one and five. The performance criterion used is the mean squared prediction error of the selected predictor. The new selection methods work well, and a comparison of results for the two different lead times underscores the need for tailoring the selection criterion to suit the lead time.