最近邻回归估计的正态逼近速度

本文利用Berry－Esseen定理，在一定的条件下，得到了最近邻回归估计逼近于正态分布的速度．     

基于因果聚类的模糊预测的择近选择

本文给出了对称三角模糊数的 Hausdauff度量的简明表示形式 ,从而解决了基于因果聚类的模糊预测的择近选择问题 .     

Computing predecessor and successor in rounding to nearest

We give simple and efficient methods to compute and/or estimate the predecessor and successor of a floating-point number using only floating-point operations in rounding to nearest. This may be used to simulate interval operations, in which case the quality in terms of the diameter of the result is significantly improved compared to existing approaches.      

NQD样本最近邻密度估计的相合性

随机变量X1，…，Xn称为是两两NQD的，若对于任意Xi，Xj（i≠j，i，j=1，2，…，n）都有：P（Xi〈x，Xj〈y）≤P（Xi〈x）P（Xj〈y）．利用两两NQD序列的Bernstein不等式，证明了两两NQD样本最近邻密度估计的弱相合性，强相合性以及一致强相合性．本文要求的条件弱于杨善朝等关于NA样本最近邻密度估计相合性所要求的条件，从而推广了杨善朝等的结果．     

Least-Squares Solution of Inverse Problem for Hermitian Anti-reflexive Matrices and Its Appoximation

In this paper, we first consider the least-squares solution of the matrix inverse problem as follows： Find a hermitian anti-reflexive matrix corresponding to a given generalized reflection matrix J such that for given matrices X, B we have minA ｜｜AX - B｜｜. The existence theorems are obtained, and a general representation of such a matrix is presented. We denote the set of such matrices by SE. Then the matrix nearness problem for the matrix inverse problem is discussed. That is： Given an arbitrary A^＊, find a matrix A E SE which is nearest to A^＊ in Frobenius norm. We show that the nearest matrix is unique and provide an expression for this nearest matrix.     

Area-interaction point processes

We introduce a new Markov point process that exhibits a range of clustered, random, and ordered patterns according to the value of a scalar parameter. In contrast to pairwise interaction processes, this model has interaction terms of all orders. The likelihood is closely related to the empty space functionF, paralleling the relation between the Strauss process and Ripley'sK-function. We show that, in complete analogy with pairwise interaction processes, the pseudolikelihood equations for this model are a special case of the Takacs-Fiksel method, and our model is the limit of a sequence of auto-logistic lattice processes.     

On certain rational quadratic forms

We give safety neighbourhoods for the necessary conditions in the change of the Jordan canonical form of a matrix under small perturbations. We also obtain the minimum distance from an n × n complex matrix which has less than k nonconstant invariant factors (2≤ k≤ n) to the set of matrices which have more or equal to k. When k= 2, we get in particular the distance from a nonderogatory matrix to the set of derogatory matrices.     

Recovery of delay time from time series based on the nearest neighbor method

We propose a method for the recovery of delay time from time series of time-delay systems. The method is based on the nearest neighbor analysis. The method allows one to reconstruct delays in various classes of time-delay systems including systems of high order, systems with several coexisting delays, and nonscalar time-delay systems. It can be applied to time series heavily corrupted by additive and dynamical noise.     

Effects of spin-wave energy degeneracy induced by in-plane wave propagation in ferromagnetic thin films

This work presents an investigation of the collapse effect predicted to occur in the spin-wave band structure of magnetic cubic thin films. The system under consideration is described by the Heisenberg model with exchange interactions between the nearest (NN) and next-nearest neighbors (NNN) taken into account. In the collapse effect, which occurs for some specific directions of in-plane propagation of spin waves, the effective coupling between spins in adjacent layers vanishes dynamically and each mode becomes confined to a single atomic plane; this means that all the bulk modes and, independently, all the surface modes become energetically degenerate. This effect of ‘directional collapse’ can only occur when the exchange interactions between NN or NNN include bonds oblique with respect to the direction of in-plane propagation.     

Lower Bounds for Local Versions of Dimension Reductions

We consider the problem of embedding vectors from an arbitrary Euclidean space into a low-dimensional Euclidean space while preserving, up to a small distortion, a subset of the distances. In particular, preserving only the distance of each vector to a small number of its nearest neighbors. We show that even when the subset of distances we wish to preserve is very small, the problem does not become easier than when one is required to preserve all the distances. Supported in part by the Israel Science Foundation.