Mean response estimation with missing response in the presence of high-dimensional covariates

This paper studies the problem of mean response estimation where missingness occurs to the response but multiple-dimensional covariates are observable. Two main challenges occur in this situation: curse of dimensionality and model specification. The non parametric imputation method relieves model specification but suffers curse of dimensionality, while some model-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) methods are the opposite. We propose a unified non parametric method to overcome the two challenges with the aiding of sufficient dimension reduction. It imposes no parametric structure on propensity score or conditional mean response, and thus retains the non parametric flavor. Moreover, the estimator achieves the optimal efficiency that a double robust estimator can attain. Simulations were conducted and it demonstrates the excellent performances of our method in various situations.     

Comparisons of normalization methods for relative quantitative in real-time polymerase chain reaction

The real-time polymerase chain reaction (rtPCR) provides sensitive and accurate quantitative results and becomes a widespread technique in analyzing gene expressions. House-keeping genes are required as references to normalize data of target genes, which may be unstable. This normalization process is similar to the normalization in analyzing high-density oligonucleotide arrays. This article evaluates the feasibility of normalizations for high-density oligonucleotide arrays to normalize data collected in rtPCR experiments. Since data features are different, simulations are used to evaluate the performance of these normalizations to rtPCR data based on five indices. Their feasibilities are illustrated by a rtPCR data.     

基于E-PASTd的盲扩频码序列估计算法

针对直接序列扩频信号的扩频码盲估计问题,改进了特征分解的方法,提出了一种基于压缩投影近似子空间跟踪的PN码恢复算法。该算法由两个主特征向量估计非同步延迟值,结合利用压缩投影近似子空间跟踪技术的快速收敛特性提取主分量,避免了对自相关矩阵的直接特征分解运算。计算机仿真表明该算法降低了数据的存储量,易于硬件实现,具有良好的收敛特性,性能优于已有的梯度算法和神经网络算法。     

基于二维主分量分析的人耳身份识别研究

有效的特征提取方法是解决人耳身份识别任务的关键之一。以主分量分析(PCA)为代表的线性子空间方法在特征提取工作中得到了广泛应用。为了更有效地提取人耳图像特征并减少运算量,将基于二维图像矩阵的2D PCA方法应用于人耳身份识别。针对三个USTB人耳图像库,采用最近邻分类器,研究了选用不同的特征维数、贡献率,及不同的相似性测度时,2D PCA方法与传统的PCA方法的识别性能。交叉验证的实验结果表明:2D PCA方法较PCA方法获得了更短的训练时间和更高的识别率,说明基于图像矩阵的2D PCA方法是一种效率更高     

模糊向量空间的有限生成子空间的一般基

提出模糊n元向量空间Vn 的有限生成子空间的一般基的概念 ,并讨论了它的一些性质 .     

复平面子空间分解的TOA无线定位方法

提出了一种利用复平面对二维移动站定位进行建模的方法,将到达时间信息转化为复数求模,通过代数运算,利用子空间的方法对目标进行定位,再利用蜂窝站址的旋转不变性对定位解进行融合,推导了复平面下TOA定位的克拉美界(CRB)。通过计算机仿真证明该方法具有较好的定位性能,在TOA测量精度较高时能够达到CRB界,有较好的应用价值。     

Between-group comparison of principal components — some sampling results

Sampling distributions arc Investigated for the critical angles proposed by Krzanowski (1979) as a means of comparing principal component analyses done on two sets of individuals, each of which has had the same p responses measured on it. A summary of the distributions is presented, by giving mean angles and 95% points. The emphasis is on the null case, when the two sets of individuals are assumed to be independent samples from the same population, but a limited study is also made of the case where the two samples come from different populations.     

一种新的子空间模式识别方法

提出了一种改进的LSM-ALSM子空间模式识别方法,将LSM的旋转策略引入ALSM,使子空间之间互不关联的情况得到改善,提高了ALSM对相似样本的区分能力。讨论中以性能函数代替经验函数来确定拒识规则的参数,实现了识别率、误识率与拒识率之间的最佳平衡;通过对有限字符集的实验结果表明,LSM-ALSM算法有效地改善了分类器的识别率和可靠性。     

基于FrFT和子空间正交的LFM信号参数估计

基于分数阶Fourier变换和子空间正交性,提出了一种低信噪比下线性调频信号检测与参数估计方法。讨论过程中将线性调频信号通过适当的分数阶Fourier变换得到一个单频复正弦信号,在此基础上,利用信号子空间与噪声子空间的正交性处理其正弦信号,再对线性调频信号的检测和参数估计转化为函数极值求解;最后,利用计算机模拟证实了方法的有效性。