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。     

明清回族进士考略(一)

本文简略考证了明清时期顺天府、直隶所属州县 29位回族进士的生平、事迹,重点提供了他们为回族的根据,以为进一步深入研究的线索。     

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.     

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。     

A test for variance-covarianch parameters in normal linear models

This paper derives a test statistic for the variance-covariance parameters which is a quadratic function of their MINQUE (Minimum Norm Quadratic Unbiased Estimation) estimates. The test is a Wald-type test, and its development closely parallels the theory used to derive a similar test for the coefficients in linear models. In fact, the derivation proceeds by first setting up the estimation problem in a derived linear model in which the dispersion parameters are the coefficients. The test statistic is shown to be the sum of the squares of independent standardized x² variables.     

Subset Selection Toward Optimizing the Best Performance at a Second Stage

In the search for the best of n candidates, two-stage procedures of the following type are in common use. In a first stage, weak candidates are removed, and the subset of promising candidates is then further examined. At a second stage, the best of the candidates in the subset is selected. In this article, optimization is not aimed at the parameter with largest value but rather at the best performance of the selected candidates at Stage 2. Under a normal model, a new procedure based on posterior percentiles is derived using a Bayes approach, where nonsymmetric normal (proper and improper) priors are applied. Comparisons are made with two other procedures frequently used in selection decisions. The three procedures and their performances are illustrated with data from a recent recruitment process at a Midwestern university.