一阶非线性中立型方程解的渐近性与振动性

本文研究一类具有交系数和多偏差的一阶非线性中立型微分方程解的渐近性与振动性     

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。     

Improved communality estimation in factor analysis

The symmetric treatment of an asymmetric approach to factor analysis is shown to provide accurate communality estimates. In comparison with existing estimates including upper and lower bounds, the present approach is shown to be superior. In one example with known communalities, the present approach perfectly captures those communalities. In two empirical examples, it is shown to produce better communality estimates than any existing method.     

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.     

加权Maclaurin对称平均及其基本性质

本文提出Maclaurin对称平均的加权形式，并得到了它的一些基本性质。     

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 .     

对称性在定积分中的应用

对称性在定积分中的应用孙军安，郑德印利用函数图形的对称性可简化定积分的计算。在特殊情况下，甚至可以求出原函数不是初等函数的定积分。因此掌握用对称性计算定积分的方法是必要的有益的。本文首先给出一个引理，由此得出一系列有趣的结果，并通过实例说明这些结果的...     

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。