一种新的高分辨定位去噪方法

高分辨定位技术在近几年得到人们的广泛注意,在这方面的研究工作相当活跃。本文提出一种新的多目标阵列接收信号协方差矩阵的去噪方法,并对结果进行了计算机模拟。     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

On Block Skip Free Transition Matrices and First Passage Times

Starting from an abstract setting which extends the property “skip free to the left” for transition matrices to a partition of the state space, we develop bounds for the mean hitting time of a Markov chain to an arbitrary subset from an arbitrary initial law. We apply our theory to the embedded Markov chains associated with the M/G/1 and the GI/M/1 queueing systems. We also illustrate its applicability with an asymptotic analysis of a non-reversible Markovian star queueing network with losses.     

Shrinkage Structure of Partial Least Squares

Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other shrinkage estimators, such as principal components regression (PCR) and ridge regression (RR), thus facilitating a direct comparison of PLS with these methods. This paper gives a detailed analysis of the shrinkage structure of PLS, and several new results are presented regarding the nature and extent of shrinkage.     

有限生成模糊向量子空间中标准向量组的求法

给出求有限生成模糊向量子空间中标准向量组的方法 .     

空间谱估计测向实验系统

空间谱估汁测向具有许多优异的性能，已得到理论分析和大量计算机模拟结果的支持。本文简要介绍一种空间谱估计测向实验系统的情况，包括测向原理、系统组成、关键技术问题分析和外场试验情况及试验结果等。实验表明，这种新型测向系统的确具有优异的性能和良好的工程实验前景。     

Banach空间中线性流形的单值度量投影算子部分

刘晶和王玉文于2001年引入并研究了Banach空间中线性流形的单值度量投影算子部分，但他们是在空间X和Y均自反、严格凸的强几何假定下来进行讨论的，这极不利于应用．我们在X和Y均是一般Banach空间的弱假定下，讨论并研究了Banach空间中线性流形的单值度量投影算子部分，并给出了该算子部分的结构的刻划．这为将比Lee S.J.与Nashed M.Z.所引进并研究的Hilbert空间集值线性映射包含的最小二乘解推广到Banach空间奠定了理论基础，所得的本质地推广了刘晶与王玉文的结果。     

Lower and Upper Bounds for Rectangular Probabilities

This article presents some results showing how rectangular probabilities can be studied using copula theory. These results lead us to develop new lower and upper bounds for rectangular probabilities which can be computed efficiently. The new bounds are compared with the ones obtained from the generalized Fréchet–Hoeffding bounds and Bonferroni-type inequalities.